A17:
TRANSPORTATION NOISE
AND VIBRATION

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022, in Gyeongju, Korea
Extended abstractABS-0012

Classification Of Road Pattern Based on Deep Learning of Big
Acoustic Data
Sang Kwon Lee1*, Jinhwan Yoo1, Chang Hun Lee1, Youngsam Yoon2, Jaehun Lee2 Kiho Yum2 andSeong-Uk Hwang3,
12

Institution, Country Mechanical Engineering, Inha University, Korea

Institution, Country Tire Design Team, Huyudai Motor Company, Korea
3

Institution, Country Research Institute, Nexen Tire, Korea

ABSTRACT
Tire-pavement Interaction Noise (TPIN) is noise caused by interactions between rolling tires and roadsurfaces. After measuring the TPIN using a microphone, transform TPIN to images using continuous wavelet
transform (CWT). The transformed images are used to classify the driving road and tire using convolutional
neural network (CNN). The Road and tire classification network using CNN is required for braking systems
in autonomous vehicle. The CNN in this paper can classify snow road, asphalt road, and two types of tires
with over 97% accuracy.
Keywords: Deep Learning, Autonomous vehicle, Noise Signal, Road and tire Interaction, Continuous
wavelet transform,
INTRODUCTION
In this study, a new road surface classification method that in-cludes snow load is presented basedon a tire–pavement interaction noise (TPIN) sig-nal and a deep learning train using a CNN architecture.
Two tires made by two differ-ent companies and two road surfaces were used for this method. Thus,
four classes were classified. The TPIN signal is obtained using a rugged microphone installed on thewheel cover, and it is transformed into 2-dimensional (2D) image data using con-tinuous wavelet
transform (CWT). The image signal is used as the input to the CNN architecture. A new CNN
architecture was constructed for the classification of two road surface types and two tires.

1. CNN
CNN is a type of artificial neural network that optimizes weights based on an in -put image feature
map. The feature map is extracted through a convolution operation between the input data and apredefined convolution kernel. CNN is widely known for its excellent perfo rmance in various imagerecognition tasks. Recently, CNN tech-niques have been applied in various fields of mechanicalengineering. Defect classifi-cation of power driving systems, prediction of tire-transmitted noise, andvehicle structure defect classification are successful applications in mechanical engineering [1-3]. The
modern concept of CNN was first suggested by LeCun et al. [4]. In that paper, Yann LeCun introduced
LeNet-5 to recognize letter patterns. LeNet-5 uses a convolution operation on a 32 x 32-pixel input
image to achieve this goal. The feature map is then compressed using a pooling operation. After
repeating the above process twice, the final feature map was unfolded to flatten and connected to a
fully connected layer. The number of nodes in the last fully connected layer was equal to the numberof classes. The values at each node were normalized between 0 and 1 using the softmax layer. Using
this normalized score, LeNet-5 can determine the class of an input letter. These suggested procedures,

from convolution, pooling, and fully connected layers to softmax layers, have become the basicarchitecture of CNN.

2. DATA
Table 1 lists the total number of images generated for each test case. The number of images is equal
regardless of the tire position if the tire type and road type are un -changed. This is because the frontwheel data and rear-wheel data are measured sim-ultaneously and controlled by the same digital signal
processor mentioned in Section 3.1. Consequently, the length of the acquired data was the same forevery measure-ment. The procedures used to create the final dataset were as follows. First, randomly
extract approximately 4000 images from one case. Second, it was divided into three subsets: one fortraining, another for validation, and the other for testing. The training set uses approximately 60% of
the total image, validation set uses approximately 20%, and test set takes the remaining. Third, thefirst two steps are repeated for all test cases. Finally, the created datasets of the same type are merged.
Two final datasets were cre-ated individually from the front-wheel and rear-wheel data.

3. CONCLUSIONSThe training results are visualized in the form of a confusion chart, as shown in F igure 1. The
diagonal elements in the confusion chart indicate the number of cor-rectly classified images. Thisimplies that the predicted class label coincides with the ground -truth label of the image. Horizontallyarranged percentage values with blue colored background are called accuracy, and vertically arranged
ones are called precision.

(a)

(b)

Figure 1. Train results for TPIN signals using CNN architecture for classificationof road surfaces and tires: (a) front-wheel data; (b) rear-wheel data.

ACKNOWLEDGEMENTS
This study was by HYUNDAI Motor Company. This study was sup-ported by a National Research
Foundation of Korea (NRF) grant pertly funded by the Korean government (MSIT)(No.
2019R1A2B5B02069400).

REFERENCES
1. Lee, S.-Y.; Lee, S.-K. Deep convolutional neural network with new training method and transfer learningfor structural fault classification of vehicle instrument panel structure. Journal of Mechanical Science andTechnology 2020, 34, 4489-4498.
2. Kim, S.; An, K.; Back, J.; Lee, S.-K.; Lee, C.; Kim, P. Health Monitoring of Power Driving System Using
Sound Signal based on Deep Learning. The Korean Society for Noise and Vibration Engineering 2021,
31, 47-56.
3. Lee, S.-K.; Lee, H.; Back, J.; An, K.; Yoon, Y.; Yum, K.; Hwang, S.-U. Prediction of tire pattern noise inearly design stage based on convolutional neural network. Applied Acoustics 2021, 172, 107617.
4. LeCun, Y.; Bottou, L.; Bengio, Y.; Haffner, P. Gradient-based learning applied to document recognition.
Proceedings of the IEEE 1998, 86, 2278-2324.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022 in Gyeongju, Korea
ABS-0015

Improving the Reliability of Brake Squeal Prediction
Zhi ZHANG1; Sebastian OBERST1,2; Joseph C.S. LAI1
1

School of Engineering and Information Technology, The University of New South Wales, ACT 2600, Australia
2

Centre for Audio, Acoustics and Vibration, School of Computer Science, University of Technology Sydney,
Australia

ABSTRACT
Despite intensive research and progress made in the past two decades, brake squeal remains a major concernof customers’ dissatisfaction, incurring significant warranty costs for manufacturers. The current industrial
practice still primarily relies on extensive noise dynamometers and/or vehicle tests. This is because it isdifficult to ascertain a-priori the reliability of brake squeal prediction which is more often used as a diagnostic
for test results rather than as a predictive design tool. By using a model of 3x3 coupled oscillators in contact
with a sliding rigid plate through friction to simulate a brake system, the effects of different friction models,different sliding speeds and uncertainties in the friction coefficients and contact conditions on unstablevibration modes are considered. Results from Monte Carlo simulations using the complex eigenvalue value
analysis illustrate that some unstable vibrations modes can be reliably predicted and that countermeasures
can be designed based on the robustness of the prediction.
Keywords: brake squeal, uncertainty, complex eigenvalue analysis

1. INTRODUCTION
Brake squeal, as a transient friction-induced self-excited vibration radiated as noise, is difficult tobe predicted reliably. This is because brake squeal is nonlinear but the traditional predictive tool for
unstable vibration modes is the linear complex eigenvalue analysis (CEA). In addition, there are alarge number of interacting factors such as operating and contact conditions, material properties,
friction and boundary conditions that are not known accurately.
The mechanical properties of the friction contact interface between the pads and the rotor areimportant factors for the generation of brake squeal (1-3) owing to the provision of friction-induced
energy (4,5) leading to self-excited vibratons. The modelling of friction can be categorised as stateand rate-dependent (6) and can be an important source of uncertainty. For state-dependent friction
models, the friction force is only related to the current brake system’s state, e.g. its relative sliding
velocity. Typical examples include the Amonton-Coulomb friction model in which the friction force
is proportional to the normal force with a constant friction coefficient (7) or the velocity-dependent
friction model in which the friction coefficient is dependent on the relative velocity (8). For ratedependent friction models, the friction history is taken into account by introducing extra time-variedinternal states to capture, e.g. the lubricant’s viscous behaviour as found for the LuGre model (6). The
bristles in the LuGre model represent the asperities of the contacting interfaces.
Apart from friction modelling, the contact between the pad and the rotor is found on multiple
length scales (9), while the pressure is inhomogeneously distributed (10), and highly sensitive tooperating conditions (11). The true contact area of an operating brake system is between 30% to 60%of the nominal contact area (12). Although the influence of the uncertainty due to the contact surface
topography on brake squeal has been analysed (3,13), there are no studies attempted to investigate the
combined effects of the variability in friction modelling and contact area on squeal propensity. Also,no general conclusion has been made on how the incorporation of an uncertainty analysis into friction
modelling and contact conditions enhances instability predictions using the linear CEA. Therefore,
the model of 3x3 coupled oscillators each in contact with a sliding rigid plate (14) is used here to1
2

j.lai@adfa.edu.au
Sebastian.Oberst@uts.edu.austudy the effects of three different friction models, and uncertainties in friction coefficients andcontact area on the prediction of unstable vibration modes.

2. COUPLED FRICTION OSCILLATORS ON A SLIDING RIGID PLATE
To study the effects of randomised friction parameters of different friction models and uncertainties
in contact conditions, an analytical model of 3x3 coupled friction oscillators first introduced by Zhang,
et al. (14) as shown in Figure 1 is used. The 3x3 coupled masses mi (i = 1…9) together represent a%
pad and each mass is in point contact through springs 𝑘!"#with a plate sliding in the x-direction witha constant speed v, which is analogue to a planar belt and whose elasticity and damping are modelledby springs (𝑘&' , 𝑘&( , 𝑘&% ) and viscous damping dashpots (𝑐&' , 𝑐&( , 𝑐&% ) . The primary characteristic of the('model is that the friction force (the friction elements 𝑓!"#and 𝑓!"#
in Figure 1) and the contact
force of each oscillator can be varied so that the effect of the local difference in friction and contact
on the global dynamics of the system can be investigated.

Figure 1 – 3x3 oscillators coupled with a sliding rigid plate through the friction force
The stiffness of the springs and damping coefficients of the system in Figure 1 are determined bycorrelating the modal frequency and modal damping of the coupled oscillator model with a real brakesystem. The 3x3 coupled oscillators simulate a pad and the plate simulates a rotor. The mass of theplate was set to be identical to that of a realistic brake rotor (9.05 kg) while the mass of each oscillator
was set to be 1/9 of the mass of a realistic pad (0.39 kg). By adopting the parametric values of thecoupled oscillators model listed in Table 1, the lowest/highest natural frequencies of the oscillators’
and plate’s modes are 1772/19700 Hz and 1063/19926 Hz respectively, which are within 1% of themodal testing results reported by Willams, et al. (15).
The equations of motion of the system in Figure 1 in matrix form and the equations of the threefriction models (namely, Amonton-Coulomb, velocity-dependent and LuGre) considered have been
given in details by Zhang, et al. (14) and will not be repeated here. The values of the parameters ineach friction model are identified from test data of a real brake system and the corresponding statistical
distributions for conducting the uncertainty analysis are estimated, the details of which have alreadybeen provided in Zhang, et al. (14). As an example, Figure 2 displays the distribution of the friction
coefficient for the LuGre model as a function of the rotor speed.

Table 1– Stiffness of springs and damping coefficients of dashpots'
𝑘!,*,+,,,#,!-'
𝑘-,.,/,0,!1,!!

(
𝑘!,*,+,,,#,!-(
𝑘-,.,/,0,!1,!!

2.45x107 N/m

1.05x107 N/m

3.05x107 N/m

3.01x107 N/m

𝑘&'

𝑘&(

𝑘&%

4.87x108 N/m

4.87x108 N/m

4.25x108 N/m

1.81x102 Ns/m

𝑐&'

𝑐&(

𝑐&%

1.55x103 Ns/m

1.55x103 Ns/m

1.55x103 Ns/m

2𝑘!"#

3.10x107 N/m
(
𝑐!"!-

%
𝑐!"!-

1.81x102 Ns/m

3.47x107 N/m'𝑐!"!-

Friction coefficient

1.81x102 Ns/m%𝑘!"#

Figure 2 – The generated friction coefficient using the constructed statistical distribution of
LuGre friction model

3. INSTABILITY ANALYSIS OF THE COUPLED FRICTION OSCILLATORS
Before instability analysis, the assumption of the system staying in the steady sliding state needs
to be tested. For a steady sliding state, the sliding speed of the plate is so fast that the oscillator motion
will never catch up with the plate’s motion resulting in the friction force never changing its direction(16). Therefore, the “stick” phase caused by the switching mechanism of the friction force can be
excluded. The threshold of the sliding speed of the plate, above which the stick-slip phenomenon
disappears, has been determined by Zhang, et al. (17) to be 0.8 km/h.
Velocity – dependent
group

Amonton – Coulomb
group
Partialcontact

Fullcontact

Partialcontact

LuGre group

Full
contact

Partialcontact

Fullcontact# Oscillators in
6contact

7

8

6

7

8

6

7

8

# Arrangement 84

36

9

84

36

9

84

36

9

Figure 3 – The hierarchy of simulations
Here, the combined effects of uncertainties in the contact conditions and the friction modelling on
instability predictions is analysed by conducting Monte Carlo simulations using the complex
eigenvalue analysis. The simulations follow a hierarchical structure as displayed in Figure 3. The“arrangement” in Figure 3 shows the choice of oscillators not in contact (the stiffness of the contact
springs connecting the corresponding oscillators with the plate are set as zero) with the sliding rigid

plate. If there are six oscillators in contact, the number of different arrangements is 𝐶/# =84. For eacharrangement, the CEA is conducted 1000 times with random friction model parameters drawn fromconstructed statistical distributions (Figure 2).

(i) Amonton-Coulomb friction model(ii) Velocity-dependent friction model(iii) LuGre friction model
Figure 4 – Boxplot of occurrence of each unstable mode (a) 9, (b) 8, (c) 7 (d) 6 oscillators in contact

Figure 4 shows the occurrence of instabilities (ie. positive real part of a complex eigenvalue) for
different friction models used: (i) Amonton-Coulomb; (ii) velocity-dependent; and (iii) LuGre. The
results indicate that the predicted instability is dependent on the number of oscillators in contact andthe plate’s sliding speed. In general, the more oscillators in contact, the higher the number andoccurrence of instabilities. The number of unstable modes predicted by the velocity-dependent andthe LuGre friction model is greater than that of the Amonton-Coulomb friction model because
additional negative damping mechanisms are introduced (Hoffmann 2007). The plate’s sliding speed
has an important effect on the frequency of occurrence of instability. When the plate’s sliding speed
increases, the number of instabilities decreases. Since the occurrence of instabilities for the plate’s
sliding speed in the interval 38-40 km/h is zero for all the contact conditions and friction models, they
are not considered hereafter.

Figure 5 – Characteristics of mode 29 with 9 oscillators in contact: mode shape and normalised median of
net work, (a),(d) Amonton –Coulomb; (b),(e) Velocity-dependent; (c),(f) LuGre friction model
Figure 5 (a), (b), (c) depict the mode shape of the unstable mode 29 for nine oscillators in contact
with the friction models’ parameters taken as the median value of the reconstructed statistical
distributions shown in Figure 2. The mode shape vector for each oscillator is given in the brackets
with three values corresponding to the displacement in the x-, y-, and z- directions respectively, andthe displacement of the four corner oscillators ie the 1st, 3rd, 7th, 9th oscillator (cf. Figure 1) in thex-direction is clearly dominant.

Figure 6 – Boxplot of occurrence of instability after constraining the 4 corner oscillators in the x-direction(a)9, (b) 8, (c) 7, (d) 6 oscillators in contact

Figure 7 – Characteristics of mode 26 with the 4 corner oscillators constrained in the x-direction
for 9 oscillators in contact: mode shape and normalised median of net work, (a),(d) Amonton –Coulomb; (b),(e) Velocity-dependent; (c),(f) LuGre friction model

4. COUNTERMEASURE DESIGN TO SUPPRESS INSTABILITY
Monte Carlo simulations of CEA and net work calculations were made by constraining the
movement of the 4 corner oscillators in the x-direction. The occurrence of instability in Figure 6 showsthat, compared to the results of the original system (Figure 4), the countermeasure is able to reduce
the probability of instability. Only mode 26 has a tendency to be unstable but its instability isdependent on the friction model, the number of oscillators in contact and the sliding speed. This mode
is not the same mode 26 of the original system, because the system has been altered by thecountermeasure. The mode shape and the normalized median of net work of the unstable mode 26 aredisplayed in Figure 7. The four corner oscillators now perform very little (not zero) net work because
the friction force in the y-direction could still perform positive work. The 4th and 6th oscillator (c.f.
Figure 1) can be seen to be dominant in contributing to the instability. Thus constraining the 4 corner
oscillators plus the 4th and 6th oscillator might further reduce the occurrence of instability. This has
been confirmed in Figure 8 showing (1) for the six oscillators in contact, there is now no instability
irrespective of the plate’s sliding speed or the the friction model, (2) mode 18 is unstable at low plate’s
sliding speeds and for all friction models but the highest median occurrence of instability is only
34.8% (LuGre friction model).

Figure 8 – Boxplots of occurrence of instability after constraining the 4 corner, 4th and 6th
oscillators in the x-direction (a) 9, (b) 8, (c) 7, (d) 6 oscillators in contact

5. CONCLUSIONS
The influence of the uncertainty in friction modelling and contact conditions on friction-induced
self-excited vibration of a 3x3 coupled friction oscillators model has been investigated. Results
illustrate that by incorporating uncertainty methods into the analysis of friction-induced instabilities,
unstable modes with consistently high occurrence of instability independent of contact area, friction
modelling and sliding speed could be identified. Such unstable modes are considered to be robustly
unstable and are most likely to produce squeal. Hence, instability countermeasures could be designedby repeating the uncertainty analysis for these robustly unstable modes. For other modes with theoccurrence of instability sensitive to contact conditions and the choice of friction models, confidencecannot be established to determine whether they are likely to squeal or not. This is no different froma deterministic analysis where it is virtually impossible to establish any confidence in identifying
which predicted unstable vibration modes will produce squeal.

ACKNOWLEDGEMENTS
The first author acknowledges receipt of a University of New South Wales University College
Postgraduate Scholarship for the pursuit of this study. The provision of experimental data to determine
the friction coefficient by Chassis Brakes International is gratefully acknowledged. This research
was supported by computational resources provided by the Australian Government through the
National Computational Infrastructure (NCI) national facility under the National Computational Merit
Scheme.

REFERENCES
1. Oberst, S., Lai, J. C. S.(2011). "Statistical analysis of brake squeal noise", Journal of Sound and Vibration
vol. 330, pp. 2978-2994.

2. Hetzler, H., Willner, K.(2012). "On the influence of contact tribology on brake squeal", Tribology
International vol. 46, 237-246.
3. Renault, A., Massa, F., Lallemand, B., Tison, T. (2016). "Experimental investigations for uncertainty
quantification in brake squeal analysis", Journal of Sound and Vibration, vol. 367, pp. 37-55.
4. Guan, D., Huang, J. (2003). "The method of feed-in energy on disc brake squeal", Journal of Sound andVibration vol. 261, pp. 297-307.
5. Oberst, S., Lai, J. C. S.(2015). "Pad-mode-induced instantaneous mode instability for simple models of
brake systems", Mechanical Systems and Signal Processing vol. 62-63, pp. 490-505.
6. Hoffmann, N. P. (2007). "Linear stability of steady sliding in point contacts with velocity dependent and
LuGre type friction", Journal of Sound and Vibration vol. 301, pp. 1023-1034.
7. Hinrichs, N., Oestreich, M., Popp, K. (1998). "On the modelling of friction oscillators", Journal of soundand Vibration vol. 216, pp. 435-459.
8. Bajer, A., Belsky, V., Kung, S.-W. (2004). "The influence of friction-induced damping and nonlinear
effects on brake squeal analysis", SAE Technical Paper 2004-01-2794.
9. Adams, G. G., Müftü, S., Azhar, N. M. (2003). "A scale-dependent model for multi-asperity contact andfriction", Journal of tribology vol. 125, pp. 700-708.
10. Zhou, K., Chen, W. W., Keer, L. M., et al.(2011). "Multiple 3D inhomogeneous inclusions in a half space
under contact loading", Mechanics of Materials vol. 43, pp. 444-457.
11. AbuBakar, A. R., Ouyang, H.(2008a). “A prediction methodology of disk brake squeal using complex
eigenvalue analysis”, International Journal of Vehicle Design, vol.46, pp. 416-435.
12. AbuBakar, A. R., Ouyang, H.(2008b). "Wear prediction of friction material and brake squeal using thefinite element method", Wear, vol. 264, pp.1069-1076.
13. Tison, T., Heussaff, A., Massa, F., Turpin, I., Nunes, R. F.(2014). "Improvement in the predictivity ofsqueal simulations: Uncertainty and robustness", Journal of Sound and Vibration vol. 333, pp. 3394-3412.
14. Zhang, Z., Oberst, S., Lai, J. C. S.(2016a) "Instability analysis of friction oscillators with uncertainty inthe friction law distribution", P I Mech Eng C-J Mec vol. 230, pp. 948-958.
15. Willams, J., Zhang, Z., Oberst, S., Lai, J. C.S. (2015). "Modal updating of brake components' influence
on instability predictions", Proceedings of 22nd Int Congr on Sound & Vibration, Florence, Italy.
16. Akay, A., Giannini, O., Massi, F., Sestieri, A.(2009). "Disc brake squeal characterization throughsimplified test rigs", Mechanical Systems and Signal Processing, vol. 23, pp. 2590-2607.
17. Zhang, Z., Oberst, S., Lai, J. C. S.(2016b). "Influence of Contact Condition and Sliding Speed on FrictionInduced Instability", Proceedings of 23rd Int Cong on Sound & Vibration, Athens, Greece.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022, in Gyeongju, Korea
Extended abstractABS-0155

Prediction of tire-induced Pass-by Noise of an electric vehicle andstudy on acoustic treatments on it
Ji Woo YOO1; Federico Di MARCO2; Min Kyoo KANG3;Ki-Sang CHAE3; Tae Sik KONG3
13

Hyundai Motor Company, South Korea
2

Autoneum Management AG, Swiss

ABSTRACTPass-by noise (PBN) of a vehicle has been always important. Especially activation of R51-03 in Europe
forces the noise should meet 68 dB for a vehicle newly produced since 2024. As an electric vehicle (EV) is
getting important in the car market, particularly tire-induced noise is a major concern to meet PBN regulation.
There have been substantial studies already by major OEMs, but mostly based on experimental ones and asimulation-based study has hardly been attempted. In this context, this study tries to find an alternative
method to reduce PBN, where so-called a source-path-receiver approach, a convenient method for simulation
by dividing source and transfer function, is taken into account. Simulation models to give acoustic transfer
function as well as acoustic source quantification by means of experimental approaches were investigated.
Different sound packages were considered and modelled for the simulation. The estimated noise curves seemto be reliable with good accuracy comparing with experimental results. Also, the study shows that thisapproach is useful because countermeasures can be handled explicitly to rank different sound packages.
Keywords: Pass-by noise, Electric vehicle, Tire noise, Transfer function, Monopoles, Sound package, FEM

1. INTRODUCTION
Pass-by noise (PBN) has been always an important issue, and especially its regulation is getting
strict in Europe with high population. Euro regulation R51-03 forces a vehicle newly produced since2024 to meet PBN of 68 dB. In addition, major OEMs expect that electric vehicles (EV) will be major
portion in future market. Considering noise from e-motor being insulated well, tire-induced noise
becomes the most important one. While most of OEMs already have carried out experimental studies,a simulation-based study seems very rare. In this context, this study tries a simulation approach thatcan be used at early stages of engineering design and to find efficient countermeasures. A mid-sized
SUV was taken into account for the study.

2. MODELLINGThe advantage of simulation may be that separation of design variables is more convenient thanexperiment. So, separation of PBN into source and path, so-call source-path-receiver scheme, isconsidered: volume source (i.e. Q) and acoustic transfer function (i.e. p/Q). How to quantify Q and
how to calculate p/Q are investigated.
Tire as a noise source may be modelled as a set of monopoles along the tire circumference. Volume
velocity Q of each monopole can be obtained from sound pressures measured in the acoustic nearfield
1

j.w.yoo@hyundai.com

2

federico.dimarco@autoneum.com

of the tire in driving conditions. In this way complexity of tire modelling greatly reduces andconsequently noise source is simplified to get better calculation efficiency. The number of monopoles
and their strengths are calculated by a mathematical approach. Figure 1 shows how monopoles are
selected. The left figure shows (reference) microphones installed in the nearfield of the tire. The
monopole candidates are placed along the tire circumference with respect to reference microphones(middle). On the right, the selected monopoles are shown where the bar chart shows the rank ofmonopoles to be selected.

Fig. 1 Measuring sound pressure with reference microphones in the acoustic nearfield of tire (left),location of possible monopoles (middle) and selection of monopoles (right).
Obtaining p/Q via exterior acoustic cavity is relatively simple. If applying exterior acoustic
packages to the vehicle is effective can be simply monitored by checking p/Q. Meanwhile reducing
strength of tire source seems very limited. The simulation model of the exterior cavity to get p/Q isshown in Figure 2 (left). The outer surface in magenta is APML (Adaptive Perfect Matching Layer)
to describe acoustic freefield. The model is made using commercial software ACTRAN. On the right,
the CAD of the exterior skin for the modelling is shown.

Fig.2 Modelling of exterior acoustic cavity (left) from exterior CAD (right).

3. TEST AND DESIGN STUDY
3.1 Experimental Validation
PBN is reconstructed by the product of Q and p/Q as explained above. Indoor tests as well as
outdoor tests were carried out to verify the reconstructed PBN. Figure 3 shows tests used for obtaining
PBN and validation.

Fig. 3 Indoor test (left) and outdoor test (right)
PBN measured in the outdoor test and the reconstructed are compared in Figure 4, which shows
this approach is reasonable and useful. As the method seems validated, one can use this approach to
reduce PBN in the framework of source-path-receiver scheme.

Fig.4 PBN measured directly in outdoor test and reconstructed. Acceleration test (left) andconstant speed test (right).
3.2 Design StudyIn this study only path, that is, p/Q is tried to be minimized by the treatment of sound packages.
The possible area for sound packages is divided into 7 local areas as seen in Figure 5. More effective
area is tried to be identified by simulation study and then the result is confirmed by the test. PBN
reduction by the acoustic treatment is compared between simulation and test (Figure 6). It can be seenthat the simulation predicts very well the reduction of PBN, and the simulation seems to be a goodtool for design study at early design stage.
Front WOLs (Wheelhouse out liners)

Battery Rear Sides

Under Engine Shield Sides

Battery Rear Middle

Under Engine Shield Middle

Rear WOLs

Battery Centre

Fig.5 Treatment of sound packages outside the vehicle.

Fig.6 Comparison of PBN reduction between simulation and test

4. CONCLUSION
This study suggests a source-path-receiver approach by combining tire source Q and acoustic
transfer function p/Q to meet European regulation for PBN. How to quantify Q and calculation of p/Q
were explained. Especially, it was shown that simulation can be a good substitution instead of test to
get p/Q and is very convenient to assess the impact of sound packages for PBN reduction.
Experimental works involving indoor and outdoor tests show that the suggested method isreasonable and gives good estimation.

REFERENCES
1. Papaioannou A, Elliott SJ, Cheer J. Application of 𝓁p norm regularisation techniques in the synthesis ofindoor tyre pass-by noise with the inverse method. J Sound Vib. 2020; 473.
2. Lafont T, Horak J, D'Amico R, Stelzer R, Bertolini C. Passive treatment solutions for the reduction ofvehicle exterior tire Noise. SAE 2018-01-1571.
3. Lafont T, Stelzer R, D’Amico R, Kropp W, Bertolini C. Modeling tyre noise in FE simulations for passby noise predictions. NOVEM 6th conference paper. 2018.
4. Yoon SH, Nelson PA. Estimation of acoustic source strength by inverse methods: part 1, conditioning of

the inverse problem. J Sound Vib. 2000;233(4):643-668.
5. Yoon SH, Nelson PA. Estimation of acoustic source strength by inverse methods: part 2, experimental
investigation of methods for choosing regularization parameters. J Sound Vib. 2000;233(4):669-705.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022, in Gyeongju, Korea
Extended abstractABS-0209

Vibration analysis for durability of EGR cooler system in dynamic
and high temperature environments
Wonseok Yang1; Jae-ha Ryu2
1
2

Department of Automotive Engineering, Korea National University of Transportation, Republic of Korea

Application Engineering of Emissions & Thermal & Turbo System, BorgWarner Chungju Ltd., Republic of
Korea

ABSTRACT
Vibration tests were performed to investigate the durability of vehicle parts against road noise and thermal
shock that occur during driving. Numerical analysis is required as actual environmental tests of vehicle partscontaining fluid flow have difficulty installing hydraulic devices. This study modeled the structural behaviorof an exhaust gas recirculation (EGR) cooler system contacting cooling water applied to base excitations byconsidering the fluid–structure interaction. A numerical model based on the finite element method wassimulated to calculate the structural displacements of the EGR cooler system and pressure fluctuations of thecooling water and exhaust gas. An excitation condition was applied to simulate a dynamic environmental test
using an experimental acceleration signal. Owing to the added mass and fluid coupling of the cooling water,
the natural frequency of the EGR system was lowered, and mode shapes were changed. Finally, the durability
life was predicted by calculating the thermal strain rate of the EGR cooler through a heat transfer analysis,
and the numerical results were verified experimentally.
Keywords: Durability, Base excitation, Thermal shock, Fluid-structure interaction, Finite element method

1. INTRODUCTION
The EGR valve system is important for reducing nitrogen oxides by recirculating exhaust ga ses.
Cooling water was used to decrease the exhaust gas temperature. Dynamic evaluations are being
performed on developed products to verify their durability against road vibrations during road driving.
A numerical analysis of the durability evaluation is essential to save the development time andeconomic costs. This study presents a simulation model for calculating the structural behaviors of an
EGR valve system considering the fluid–structure interaction. In addition, the durability performanceof the EGR valve system was predicted to calculate the thermal stress using a heat transfer analysis.

2. NUMERICAL ANALYSYS
2.1 Vibration Analysis for Road Excitation
A numerical simulation based on the finite element method was performed for vibration analysisof the EGR valve system. The finite element equation considering the fluid–structure interaction
becomes:0  d  Cs
 Ms
 S M    +  0
f  p  f

0  d  K s
 +C f  p   0

−ST  d  f   =  
K f  p  0

(1)

where Ms , Cs , and Ks are the global mass, damping, and stiffness matrices of the solid, respectively;
M f, C f, and Kf are those of the fluid, respectively; S is the interaction matrix calculated using theboundary condition and the selected shape function. For a simple harmonic analysis, the displacement,
1
2

wsyang@ut.ac.kr
jhryu@borgwarner.com

pressure, and force matrices were assumed to be the frequency response terms. The finite element
equation in the frequency domain was obtained using equation (1) to calculate the mode shapes andtransfer function for the road excitation.

Figure 1. Schematics of the EGR valve system
Mode analysis of the EGR valve system was performed using equation (1) in the frequency domain.
The torsional and bending modes of the coolant tank box are the main parameters influencing the
durability of the EGR valve system. To verify the frequency response function of the simulation, theacceleration responses of an actual EGR valve system were measured using a shaker andaccelerometers. The torsional and bending modes of the actual system were measured and were similar
to those of the simulations.
2.2 Heat Transfer Analysis for Thermal ShockUsing the equation of heat transfer, the finite element equation was obtained as follows:
Cta

 0
 0


0
Cts
0

0   t a   K ta + h1S1
  
0   t s  +  − h1S1
Ctf  t f  0−h1S1
K ts + h1S1 + h2S 2 + h3S3
−h3S3

  t a   f1 0  
− h3S3   t s  = h2S T 
K tf + h3S3  t f  
 f2 

(2)where C ta, Cts, and C tf are the specific heat matrices of gas, structure, and water, respectively; Kta,
Kts , and Ktf are the conductivity matrices of gas, structure, and water, respectively; S1 , S 2, S 3, and S∞
are the interaction matrices of gas, structure, water, and free stream air, respectively. f 1 and f 2 are theheat flux vectors of the inlet gas and coolant water, respectively. The thermal stress was calculatedusing the coefficient of thermal expansion and the predicted temperatures.
The measured temperature profile was used to model the thermal shock. Explicit direct integrationof equation (2) was performed when calculating the stress on the structural surface of the EGR valve
system. When a thermal shock is generated, the inlet pipe of the EGR valve system has the hightemperatures, which cause stress concentrations. In the experiments, the temperature distribution of
the EGR valve system was measured using a thermographic camera. The results of the experiments
were similar to those of the numerical model.

3. DURABILITY EVALUATION
Endurance analysis was performed using Miner’s rule to evaluate the EGR valve system durability
exposed on road excitation and high–temperature environments. A rainflow counting algorithm wasused to predict the number and amplitudes of varying alternative stresses during a single cycle ofdynamic high–temperature conditions. Considering the numerical results, the damage rates of Miner’s
rules were less than one when repeating 10,000 cycles of loading of vibration and thermal shock. The
experimental results indicated that cracks were not generated in the EGR valve system.

4. CONCLUSION
This study presents a durability evaluation method for investigating an EGR system subjected toroad vibration and thermal shock. Using simulations based on the finite element method, thedisplacement, pressure, and temperature responses were calculated and verified based on theexperimental results. To evaluate the structural durability, the time responses during a single cycle ofdynamic temperature conditions were predicted using the time iteration method. When repeating
10,000 cycles of structural and thermal loading, the damage rates of the EGR valve system were within
the safe range that can endure road vibrations and high–temperature conditions. The numericalmethodology can be utilized to predict the durability and fatigue life of complex structural syste ms inharsh environments.

ACKNOWLEDGEMENTS
Following are results of a study on the "Leaders in INdustry -university Cooperation 3.0" Project,supported by the Ministry of Education and National Research Foundation of Korea.

REFERENCES
1. Demirkesen C, Zeren HB, Guryuva S, Savci IH. Numerical and experimental investigation of heavy-duty
EGR cooler thermal performance. Appl Therm Eng 2022;22:118531.
2. Yang W. Prediction of flow velocity from the flexural vibration of a fluid-conveying pipe using thetransfer function method. Appl Sci. 2021;11:5779.
3. Dhummansure V, Salunkhe PS, Doddamani S, Jamadar NI, Structural analysis and optimization of EGR
cooler for diesel engine. J Fail Anal Prev. 2021;21:1387-95.
4. Park J, Cho J, Choi H, Park J. Prediction of reformed gas composition for diesel engines with a reformed
EGR system using an artificial neural network. Energy 2020;13:5886.
5. Kim Y, Kim U. Effects of torsional vibration of a propulsion shafting system and energy efficiency designindex from a system combining exhaust gas recirculation and turbocharger cut out. J Mech Sci Technol.
2019;33(8):3629-39.
6. Seo Y, Heo S, Kwon Y, Ku T, Kim J, Kang B. A Study on thermal and modal characteristics for EGR
system with dimpled rectangular tube. J Korean Soc Precis Eng. 2008;25(3):115-25.
7. Cook RD. Concepts and applications of finite element analysis. 4th ed. New York, USA: Wiley; 2002. p.
373-488.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022 in Gyeongju, Korea

ABS-0238

Characterizing the noise emission of an electric powertrain intime/rpm domain using 3D sound intensity
Engin GÜVEN1; Dani FERNANDEZ COMESAÑA2; Tales MARINI STORANI3
1

Hyundai Motor Europe Technical Center GmbH, Germany
2,3

Microflown Technologies, the Netherlands

ABSTRACTSound intensity mapping contributes to a comprehensive analysis of the radiated noise generated by acomplex mechanical system, such as an electric powertrain. The numerical simulation of an accurate
physical-based mathematical model is prohibited due to the complexity of the mechanical systems and theirhigh computational cost. Consequently, a full data-driven three-dimensional representation is often preferred(if available) in most industrial applications. Usually, those data-driven approaches utilize measurements
acquired with large sensors arrays. However, it’s also possible to capture the necessary information by
performing multiple subsequent measurements synchronized by an external reference signal. In this paper,
we present a novel technique using multiple 3D sound intensity probes and a three-dimensional tracking
system to measure the sound radiated by an electric powertrain. Free-field data is combined with vehicletransfer functions to synthesize the sound pressure contribution to the vehicle passengers. Results reveal thedominant noise sources at different operational regimes with a high spatial resolution, enabling the design oftargeted noise reduction strategies based on the captured data.
Keywords: Sound Intensity, Electric Powertrain, Sound Power.

1. INTRODUCTION
The vibro-acoustic properties of a car cabin play a key role in the perception of vehicle quality.
Increasing pressure from weight and emissions targets means that automotive OEMs are being
challenged to meet customer expectations for NVH performance in ever more efficient ways without
inflating the cost of production.
The rapid development in computational processing power has enabled the use of numericalsimulations to determine the dynamic response of complex structures with reasonable accuracy [ 1-3].
These techniques are effective especially in the early stages of the design cycle. However, substantial
discrepancies are often found between simulations and experiments which led to the development ofhybrid techniques [4] or purely experimental methods [5].
There is a growing trend of developing testing solutions for evaluating the acoustic performanceof separate components before they are assembled in the vehicle. The use of measurable physicalquantities in combination with well-defined signal processing algorithms has proven reliable toidentify and quantify vibro-acoustic problems [6].
Any acoustic field can be described by the sound pressure and particle velocity spatial variations.
Three-dimensional (3D) sound intensity measurements are able to capture all available acoustic
information, providing a unique starting point to solve complex problems. This paper evaluates theusage of 3D sound intensity measurements performed in an engine dyno in combination with vehicletransfer functions to both localize the main noise sources around the powertrain as well as to predict
their impact towards the vehicle passengers.

egueven@hyundai-europe.com
fernandez@microflown.com
3
storani@microflown.com
12

2. Sound intensity-based panel noise contribution analysis
The sound pressure 𝑝ref located at the reference position 𝐱 due to a continuous radiating surface
𝑆 can be derived from the Kirchhoff-Helmholtz Integral Equation using Green's functions [7]𝑝ref (𝐱) = ∫ (𝐺(𝐱, 𝐲)𝑆𝜕𝑝(𝐲)𝜕𝐺(𝐱, 𝐲)− 𝑝(𝐲)
) d𝑆,
𝜕𝑛
𝜕𝑛

(1)

where 𝑝(𝐲) is the sound pressure at position 𝐲 on the surface 𝑆, 𝜕/𝜕𝑛 denotes normal derivative on
the surface and 𝐺(𝐱, 𝐲) is the Green function that relates the propagation between the points 𝐱 and𝐲. Under free-field conditions, Green's function can be determined via analytical solutions. However,this is no longer the case for most practical scenarios. Alternatively, Green functions can also be
acquired experimentally by measuring the acoustic pressure perceived at a point 𝐱 in response to amonopole sound source. The above expression can be simplified by assuming that the data is acquired
directly at a rigid boundary[8]. Such an approximation, often referred to as the hard-wall assumption,
is used by most pressure-based panel contribution methods. For the practical implementation of
Equation 1 it is necessary to subdivide the surface 𝑆 into 𝑁 small sections of area Δ𝑆𝑖 . Consequently,
Equation 1 can be written in a discrete form as𝑁
𝑝TF (𝐲𝑖 )𝑝ref (𝐱) ≈ ∑ (𝑢 (𝐲 )) Δ𝑆𝑖 .
(2)
𝑄(𝐱) 𝑛 𝑖𝑖=1
The above expression enables the reconstruction of the sound pressure 𝑝ref at a reference position
𝐱 by measuring the sound field at 𝑁 measurement points. When assessing the noise radiated by acomplex structure, such as an electric powertrain, it is convenient to study the contribution of thedifferent constructive elements. The acoustic contribution 𝑝cont of a single component can becomputed by adding a sub-set of measuring positions surrounding the element. A similar expression
can be derived following the substitution monopole technique theory, where each panel energy isdefined by their locally measured sound power, i.e.
2

κ0 𝑝TF (𝐲𝑖 )(3)|𝑝cont√𝑊(𝐲𝑖 ))|
≈ ∑ |(
ω 𝑄(𝐱)
i=1
where κ0 is a weighting factor common for all panels. It should be noted that the proposed expression
combines sound intensity together with measured acoustic transfer functions, data that should be
captured into two separate steps.
(𝐱)|2

N

3. Measurement system: Acoustic ShapeThe sound visualization system Acoustic Shape was used to capture the information hereby
presented. This system enables the visualization of high-resolution sound intensity 3D vectors evenfor time-varying operational regimes. As a result, both sound localization, as well as noise
quantification, can be performed directly in all sub-components of the powertrain. The measuringsystem comprises the following hardware elements:• 3D intensity probes: each consisting of a pressure microphone and 3 particle velocity sensors.
• Signal conditioner(s): a unit per probe for supplying power and pre-amplification.
• 3D tracker: automatic real-time 3D position and orientation using a stereo infrared camera.
• Tracking sphere: open sphere with IR markers for tracking the probe array.
• Remote handle: to control and monitor the data acquisition process.
• Data acquisition unit: a 24-channel Heim DATaRec 4 DIC24 was used in this investigation.
Before measuring the acoustic field, it is required to have a 3D model of the testing object. The
model can be either imported from a CAD file or obtained by scanning the area of interest with astructural scanner. The model is used as a visual reference as well as to calibrate the 3D tracker
position within the measuring environment.
The probe array can be positioned arbitrarily within the target area, provided that remains in the
3D tracker field of view. As a result, real-time positioning information becomes available for
performing point-by-point measurements in a fast and efficient way. The recorded signals aresynchronized using a external signal present during all the captures. For this investigation, the RPM
signal measured at the dyno shaft was used. The visualization of the sound pressure, particle velocity,or sound intensity field can then be computed to provide a 3D representation of the sound spatial
distribution during the operational cycle.

4. Experimental investigation
The measurement campaign evaluated in this study focuses on the noise produced by an electric
powertrain while running for wide-open throttle (WOT) followed by a slow run down (CT). Firstly,
operational experiments were carried on a dyno test bench inside a semi-anechoic chamber at thetesting facilities of ATESTEO Technologies GmbH. Capturing the free-field radiation allowed for
identifying key radiation areas and characterizing the acoustic emission across the entire unit withhigh spatial resolution. Secondly, in-vehicle transfer function between the powertrain and thepassenger listening positions were performed at HMETC (Rüsselsheim am Main, DE). Data from bothmeasurements were combined to predict the sound inside the cabin interior. Figure 1 shows a pictureof the array along with a preview of the 3D model of the powertrain measured.

• EDU• Junction Box• Inverter

Figure 1. Picture of the array consisting of 5 sound intensity 3D probes (left) and sketch of the electric
powertrain investigated (right), highlighting the positions around the different sub-systems.
4.1 Sound pressure synthesis
At an early development stage of an electric powertrain, it’s crucial to identify potentiallyproblematic frequency regions that can be annoying inside the cabin interior. Sound intensity dataacquired near the radiating surfaces was combined with spatially averaged vehicle transfer functions
using Equation (3). This approach allows for predicting the acoustic contribution of t he powertrain
towards the vehicle passengers. Figure 2 shows an example comparing the sound pressure spectrogram
inside the cabin for a full-vehicle measurement and a synthesized contribution of the powertrain alone.
Although multiple additional sources of sound are present during the full -vehicle measurement, thesynthesized prediction allows to identify critical excitation areas which can be problematic inside thecabin. It should be noted that the most characteristic powertrain orders can be accurately predictedwith the proposed sound intensity-based contribution technique.
Synthesized Powertrain Contribution

Frequency [Hz]Frequency [Hz]Full Vehicle Interior Noise

Figure 2. Sound pressure at the cabin interior during WOT run-up measured in a roller test bench (left) andsynthesized powertrain contribution (right).

4.2 Sub-component contribution and ranking
There are three main sub-components of the electric powertrain responsible for most of the acoustic
radiation: power inverter, junction box and electric driving unit (EDU). After finding a potentiallyproblematic frequency range, it is then key to identify the relative dominance between components.
Figure 3 shows the synthesized sound pressure level of the different components across the entire
WOT run-up filtered between two different frequency bands: mid-low and high frequencies (exact
bounds are not disclosed for the sake of confidentiality).
High Frequency Range

Pressure Contribution [dBA]

Pressure Contribution [dBA]

Mid-Low Frequency Range

RPM

RPM

Figure 3. Band-pass filtered sound pressure contribution of different powertrain sub-components.
4.3 Sound field visualization
Besides the quantification and ranking inside the cabin interior, the captured data can be very
valuable to troubleshoot problems inferred from the analysis presented above. Conventionally, whena problem is found during a full vehicle test, data from the powertrain will consist of a spectrogramof the sound pressure measured at each motor side or a beamforming image with very limited dynamic
range. Therefore, for most issues, such datasets would not be enough to find a solution or gain further
understanding about the problem. In contrast, having measured 3D sound intensity at multiple points
very close to the radiating surfaces provides much more information about a complex problem for theentire spectrum with a large dynamic range. Figure 4 illustrates an example of the sound intensity
field captured during the run-up focused on the mid-low frequency range. As shown, for the displayed
RPM range, the top side of the inverter is the element with the most significant radiation.

Noise

Figure 4. Sound visualization of the 3D active intensity during WOT

5. Conclusions
A novel measuring technique suitable for characterizing the acoustic response of an electric
powertrain is hereby introduced. Sound intensity vectors captured in a semi-anechoic testing room are
combined with reciprocally measured transfer functions to quantify the sound pressure contribution
towards the cabin interior. Capturing acoustic data directly near the radiating surfaces allows for
obtaining a detailed representation of the acoustic field created by the different powertrain sub systems such as the inverter, junction box and electric drive unit (EDU). The resulting high spatial
resolution vector maps can be very useful for troubleshooting frequencies/operational conditions ofinterest. On the other hand, the combination of near-field data with full vehicle transfer functions
enables to quantify the acoustic contribution of each of the sub-components towards the vehicle
passengers, anticipating any potential issues at a very stage of development.

REFERENCES
1. Wang, Binxing, Sifa Zheng, Lin Zhoul, Shengqiang Liu, Xiaomin Lian, and Keqiang Li. "Acoustic
modelling and analysis of vehicle interior noise based on numerical calculation." In Intelligent
Computation Technology and Automation (ICICTA), 2010 International Conference on, vol. 1, pp. 404407. IEEE, 2010.
2. Zheng, Ling, Zhanpeng Fang, Zhongcai Tang, Zhenfei Zhan, and Jiang-hua Fu. The design optimizationof vehicle interior noise through structural modification and constrained layer damping treatment. No.
2015-01-0663. SAE Technical Paper, 2015.
3. Georgiev, Vasil B., R. L. Ranavaya, and Victor V. Krylov. "Finite element and experimental modellingof structure-borne vehicle interior noise." Noise Theory and Practice 1, no. 2 (2) (2015).
4. Siavoshani, Saeed, Jay Tudor, and Dev Barpanda. Vehicle body optimization of structural noise andvibration using a hybrid technique. No. 2007-01-2327. SAE Technical Paper, 2007.
5. Furukava, Márcio, Samir Gerges, Miguel M. Neves, and Bento JL Coelho. "Analysis of structural
damping performance in passenger vehicles chasis." J. Acoust. Soc. Am 126 (2009): 22-80.
6. Fernandez Comesaña, D., Carrillo Pousa, G., and Tijs, E., "Integration of an End-of-Line System for
Vibro-Acoustic Characterization and Fault Detection of Automotive Components Based on Particle
Velocity Measurements," SAE Technical Paper 2017-01-1761, 2017.
7. E. G. Williams, “Fourier Acoustics: Sound Radiation and Nearfield Acoustical Holography,” Academic
Press, 1999.
8. O. Wolff , “Fast panel noise contribution analysis using large PU sensor arrays," in Proceedings ofInternoise, 2007.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022 in Gyeongju, Korea

ABS-0318

Sporty Muffler Note Requirement for Indian Customers
Mugilan M1; Arunsubramaniam L2; Vignesh R3
1

Hyundai Motor India Engineering Private Ltd (HMIE), India

2

Hyundai Motor India Engineering Private Ltd (HMIE), India

3

Hyundai Motor India Engineering Private Ltd (HMIE), India

ABSTRACTThe demand & expectation of sporty cars for the Indian market has been increasing in recent times. Apartfrom design & aesthetic changes, dynamic parameters like engine performance, Ride & Handling & Muffler
noise differentiate a sporty car from a normal car. Among this sporty muffler note is a head-turner, as it grabs
the attention of passengers & other people on the road. Indian pass-by noise regulation policies (IS 3028:1998(2)) were made stringent to limit muffler noise. The Current sport line muffler note though not significantly
loud, has appealed & attracted customers. In this paper, The Indian customer’s driving pattern at City &highway conditions are analyzed, then an extensive study is done on the kind of muffler exhaust note (RPM
zone & noise type) preferred. Accordingly, a guideline of appropriate sporty muffler note for Indiancustomers is concluded which will satisfy both sporty tone & also meet pass-by noise regulation.
Keywords: Sporty, Muffler Note, City & Highway.

1. INTRODUCTION
The prime motive behind improving Noise & Vibration in passenger vehicle is to eliminate or
reduce any unwanted Noise & Vibration. The customer perceives any unexpected Noise & Vibration
as serious concern towards failure (1). The owner of a luxury vehicle expects the Noise to be lower,
whereas that of a sports car expects a loud & exciting note from the exhaust muffler on every dab ofthrottle. Hence, the sound quality of the Power Train system has a significant influence on a car. So,
during development utmost importance is given to fixing the type & level of Engine & Exhaust noisebased on target customers.
This paper begins with a review of trends in the Indian market towards sport line cars (Economy
class). The difference between a normal & sport line car across different OEMs, customer & media
opinion towards them. A brief discussion on the Indian customer driving cycle in City & Highways isalso covered since this lays the foundation to create the net engine sound level & character perceived
by the driver & occupants in the vehicle. Then an extensive study is performed to understand thedifference in sporty notes between N Line (Sporty) & normal muffler in the described Indian driving
conditions. Based on the study results, the development direction of the sporty Muffler note is
proposed which best suits the Indian Customers.

2. NEED FOR SPORTY MUFFLER NOTE
2.1 Demographics & Economy of India
India is the second most populated & currently is about one-sixth of the world population, with themedian age of the Indian Population being around 28.7 years. The current Indian population is moreconcentrated towards age group between 20 to 34 years.
The Indian Economy is the 5th largest by nominal GDP & 3rd largest by Purchasing Power Parity.
The Combined Effect of the growing Population & Economy has made, The Indian Automotive
mugilan.murugan@hmie.co.inarun.s@hmie.co.in3
vigneshr@hmie.co.in
1
2

Industry 4th largest in the world (as per 2021 statistics) & 5th largest in terms of sales (2020).
Table 1 – Automobile Domestic Sales (in Numbers)
Category

2019-20

2020-212021-22

Passenger Vehicles

2,773,519

2,711,457

3,069,499

The long term growth perspective of the Indian Economy remains positive due to its young
population with a low dependency ratio, increasing globalization & integration into the global
economy. The Indian automobile industry is expected to grow further & dominated by the likes &trends set by the young population.

Figure 1 – Indian Population Pyramid in 2020 (3).
Economy Class Sporty Cars before N Line Entry
India with a dominant young population always has high expectations of passenger cars. They arevery price sensitive & consider value for money as a crucial factor in terms of quality, innovation,
safety, features & status. They even tend to extend the budget, if the product is worth the consideration.
If it doesn’t relate, it creates disappointment & negative marketing among the potential group ofbuyers. Though economy & family sedans & SUV dominate the market, sports car stands out due totheir driving satisfaction & attention grabbing characteristics. But sports cars are more expensive dueto the use of powerful engines, Technology, accessories, wheels & cost of maintaining them. This led
manufacturers to provide a sportier version of normal cars as they are inexpensive & affordable.
To differentiate the sportier version from its regular version minor visual changes are made bycompetitors such as inserts in the front & rear bumper, wheel covers, body graphics & paint finish on
outside mirrors. Media & customers disliked & criticized this approach as no changes were made interms of dynamic tuning. The comment from renowned media about a leading manufacturer is asfollows:“abc had been infamous for performing cosmetic brutalities on their existing models - one suchgarish contraption was christened the xyz in mid-2013: Obviously, there was nothing "xyz" about thexyz, other than visual changes that also included painted wheel caps!” ~ Team-BHP.
2.2

2.3 N Line Entry
Apart from aesthetic & design changes customers also expect changes in terms of dynamic tuning,which provides better driving pleasure in terms of Engine Performance, Ride & Handling & Muffler
exhaust notes. Though the sporty car has an appealing difference in terms of design & aestheticchanges but is limited as normal cars get much more in terms of dual -tone colors, and exterior stylingwhich gets revised every two years in the form of Facelifts, Product Enhancement (every 4 years) &Anniversary editions. As far as Engine performance is concerned, even normal cars are equipped with

Turbo Petrol engines which are powerful & have good performance. Due to stringent emission norms(Real Drive Emission Norms), bigger petrol & diesel engines are replaced by smaller Turbo Petrolengines (refer Table 2), which is not catching lot of attention among customers. Moreover, powerfulturbo engines will increase the cost & reduces fuel efficiency. So, manufacturers rule out the optionof providing more powerful engines for Economy class Sport line cars.
Table 2 – Mileage & Power comparison of Turbo & Naturally Aspirated (NA) Engine
EnginePower (PS)Torque (Nm)

ARAI Mileage (Kmpl)

1.0 Turbo Petrol

120

143.8

19.2

1.5 NA Petrol

115

172

17.7

Earlier due to poor road conditions such as poor road patches & potholes, lower speed limit of
80kph & availability of less powerful engines expectation on Ride & Handling package is towards
more ride comfort & compromise towards handling was acceptable.
Currently, due to the rapid development of expressways & shift in the driv ing speed limit from 80 to
100 kph, it has raised the expectation of customers & media towards more handling oriented . So forbetter ride, compromise on handling is not much accepted. This reduces the room for further
improvements in handling during the development of the sport line car. Thus the only parameter thathas a significant difference between normal & sporty cars is the Exhaust note from mufflers. The
Exhaust muffler note adds flavor to the sportiness of the car, it not only grabs the attention of thedriver, and passenger inside the car, but also creates a head-turner situation for the people on the road.
Also, noise from the muffler is one of the component of noise pollution & are to be developed to meetthe noise regulation set (IS 3028:1998). Though customers can modify or tune muffler notes as per
their requirement from aftersales customization. But, as per Section 190 of the Central Motor Vehicle
Act, Usage of modified exhaust mufflers violating standards prescribed concerning control of air &noise pollution is punishable & will be charged a fine of Rs 1,000 & for subsequent violation with afine of Rs 2,000.
So, developing an exhaust muffler that meets the stringent pass-by noise norms & offering a sporty
note has become a tedious task for Automobile manufacturers.
To overcome the above challenges, Hyundai introduced the N Line model (Economy class sportscar) to offer a sporty experience like never before to Indian customers. The N Line model is based on
an existing product with tuned dynamic performance, styling & sporty sound from the exhaust.

3.

INDIAN CUSTOMER DRIVING PATTERN

Road Network has become the most important mode of transportation in India. The Indian Road
network has been expanding at a rapid pace & is currently the 2nd largest in the world. To understandthe customer driving pattern, an insight into different road conditions is of prime importance. The
Indian road condition is broadly classified as City & Highway.
Based on the PAN India driving investigation data measured using INCA (Engine relatedparameters data logger), Following Tables 3 & 4 lists the most widely used driving pattern in India.
Table 3 – City driving pattern
Gear

Vehicle Speed (kph)Engine Speed (rpm)1

0 to 25

900 to 2250

2

10 to 35

1000 to 2050

3

20 to 60

1200 to 2500

430 to 901250 to 2400

Table 4 – Highway driving pattern
Gear

Vehicle Speed (kph)Engine Speed (rpm)325 to 70

1200 to 2700

4

40 to 110

1450 to 2800

550 to 115

1650 to 2800

6

60 to 130

1750 to 3200

In city driving pattern, Constant throttle input is very less or not practical due to more traffic
congestion, less lane discipline & no designated lane for two-wheelers & cyclists. A frequent stop &go pattern is observed due to traffic where a quick initial throttle response from 0 kph is preferred.
Due to increasing fuel costs & Indian Customers being more conscious about saving fuel, High Gear
Low RPM (HGLR) condition is predominately driven in City. The common phenomenon observed on
Highways is frequent downshift required for overtaking, braking (cattle movement) & acceleration.
The above are the major findings of the Indian driving cycle in cities & highways.
In the upcoming section, a comparative study of the exhaust notes between N Line & normal
muffler simulated in the described driving condition (gear & Engine speed) is discussed with the helpof objective test results recorded using LMS.

4. N LINE Vs NORMAL EXHAUST NOTE COMPARISON
4.1 Measurement methods
To capture & compare the note of exhaust muffler, a passenger vehicle with one-liter Turbo
Gasoline 3-cylinder engine is used for measurement after the engine is warmed to optimum driving
conditions. For a 3-cylinder engine, the main components of muffler noise are Tailpipe Exhaust Noise& Booming (1.5th order) were recorded using microphones in the front (driver ear level) & rear (center
passenger ear level) of the cabin. The test conditions were used from table 3 & 4.
4.2 Results & Discussion
To propose a development direction of sporty muffler notes for Indian customers, N Line & normal
mufflers were employed to study the influence of driving patterns on exhaust notes. Comments fromvarious media on the N-line muffler note have been gathered for reference.
Experimental test results comparing N Line & Normal mufflers has been shown in Figure 2 to 8.

1.00

1.00

Amplitude

Pa
dB(A)

Amplitude

60.00

Pa
dB(A)

60.00

40.00

0.00

40.00

0.00

900.00rpm
3000.00
XSI 1::EMS_DCT1::N (CH10000) ; XSI 1::EMS11::N (CH10002)(a) Front (Muffler Note)rpm
900.003000.00
XSI 1::EMS_DCT1::N (CH10000) ; XSI 1::EMS11::N (CH10002)(b) Rear (Muffler Note)
N Line Muffler
Normal Muffler

Figure 2 – Comparison of sporty noise between N Line & Normal Muffler (city condition – 1st gear)

1.00

1.00

Amplitude

Pa
dB(C)

Amplitude

100.00Pa
dB(C)100.00

40.00

0.00

40.00

0.00

900.00rpm
3000.00
XSI 1::EMS_DCT1::N (CH10000) ; XSI 1::EMS11::N (CH10002)

900.00rpm
3000.00
XSI 1::EMS_DCT1::N (CH10000) ; XSI 1::EMS11::N (CH10002)(a) Front (Muffler Note)(b) Rear (Muffler Note)

Figure 3 – Comparison of Booming between N Line & Normal Muffler (city condition – 1st gear)

0.00

Hz
Frequency

1100.00

dB(A)
Pa

10.00

rpm

XSI 1::EMS11::N (CH10002)

rpm

XSI 1::EMS_DCT1::N (CH10000)

1100.00

60.00

3000.00

dB(A)
Pa

60.00

3000.00

10.00

0.00

1000.00(a) N Line Muffler (Front)

Hz
Frequency

1000.00(b) Normal Muffler (Front)

Figure 4 – Color Map Comparison (city condition – 1st gear)
It can be found from Figures 2 (a), and (b) that the muffler note of the N Line is slightly high comparedto Normal, this can also be confirmed from the color map comparison in Figure 4. Subjectively a mild
muffler growl is observed between 1500 to 2000 rpm & Mild Booming between 1700 to 1900 rpm.
This phenomenon can be frequently felt during city drives where a quick initial throttle is performed
from 0 kph. However, slightly louder muffler notes or muffler growl being extended till 2500 rpm
would cause excitation for drivers.
1.00

Pa
dB(A)

Amplitude

1.00

Amplitude

70.00

Pa
dB(A)

70.00

50.00

0.00

50.00

0.00

1000.00rpm
3000.00
XSI 1::EMS_DCT1::N (CH10000) ; XSI 1::EMS11::N (CH10002)(a) Front (Muffler Note)rpm
1000.00
3000.00
XSI 1::EMS_DCT1::N (CH10000) ; XSI 1::EMS11::N (CH10002)(b) Rear (Muffler Note)

Figure 5 – Comparison of sporty noise between N Line & Normal Muffler (City – 4th gear)
Also, from Figures 5 (a) and (b) it can be seen that in HGLR condition (City – 4th gear) only minutedifference in muffler noise is observed between 1200 rpm to 1700 rpm (both in front & rear). A
distinguishable muffler growl or rumble in the above driving range would be preferred by enthusiastic.
As most Indian customers prefer to drive at HGLR to be fuel efficient .

1.00

1.00

Amplitude

Pa
dB(A)

Amplitude

80.00

Pa
dB(A)

80.00

30.00

0.00

40.00

0.00

1000.00rpm
5000.00
XSI 1::EMS_DCT1::N (CH10000) ; XSI 1::EMS11::N (CH10002)

rpm
1000.00
5000.00
XSI 1::EMS_DCT1::N (CH10000) ; XSI 1::EMS11::N (CH10002)(a) Front (Muffler Note)(b) Rear (Muffler Note)

Figure 6 – Comparison of sporty noise between N Line & Normal Muffler (Highway – 3rd gear)

1.00

1.00

Amplitude

Pa
dB(C)

Amplitude

100.00Pa
dB(C)100.00

40.00

0.00

40.00

0.00

1000.00rpm
5000.00
XSI 1::EMS_DCT1::N (CH10000) ; XSI 1::EMS11::N (CH10002)

1000.00rpm
5000.00
XSI 1::EMS_DCT1::N (CH10000) ; XSI 1::EMS11::N (CH10002)(a) Front (Muffler Note)(b) Rear (Muffler Note)

Figure 7 – Comparison of sporty noise between N Line & Normal Muffler (Highway – 3rd gear)

0.00

Hz
Frequency

1000.00(a) N Line Muffler (Front)

1200.00

dB(A)
Pa

10.00

rpm

XSI 1::EMS11::N (CH10002)

rpm

XSI 1::EMS_DCT1::N (CH10000)

1200.00

60.00

5000.00

dB(A)
Pa

60.00

5000.00

10.00

0.00

Hz
Frequency

1000.00(b) Normal Muffler (Front)

Figure 8 – Color Map Comparison (Highway – 3rd gear)
As far as Highway driving pattern is concerned, third gear exhaust note data is shown in Figures 6 to
8 as it is the frequent used gear for hard acceleration during highway drives & test mode in Pass by
noise test. It can be seen from Figures 6 to 8 that no difference in muffler noise is observed & muffler
booming of normal muffler is higher, which feels sporty & not annoying. Though during long highway
drives, louder muffler notes & booming could create headaches and nausea for passengers which had
been incorporated during development. But a distinguishable difference in muffler noise or boomingin N Line muffler would create excitement during acceleration in highways. As both the muffler has
passed the pass by noise test & with room for improvement in muffler note, direction towards slightly
louder muffler note would not deviate it from the pass by noise norms.

4.3 Media Opinion & Sales Volume of N Line
From the above test results, Though N Line mufflers are tuned to be louder in certain driving
pattern like city 1 st & 2 nd gear, consideration of customer’s uneasiness towards long highway drives
is studied & incorporated. But still there is scope for improvement in terms of providing better muffler
note & booming especially in HGLR condition & highways where customer can appreciate sporty
note of muffler in N-line car
Though a lot of studies has been carried out during the development of N Line sporty note, it ismandatory to understand the pros & cons from the media & customer perspective. Based on their
feedback development direction can be modified to offer a better product to customers.
Table 5 – Media review towards N Line muffler exhaust note
Magazine

Comments

Zigwheels

During drives it’s lot quieter in city & highways speeds, until revved above 4000 rpm.

CarDekho

New exhaust adds flavor to engine at idle. Could have been more audible

Overdrive

Exhaust note is mildly better, not loud enough to dominate cabin conversation

Team-BHP

3 cylinder at idle is accompanied by a faint yet distinct burble from the exhaust pipes.

From Table 4, it is seen that media opinion towards the N Line muffler is similar to the study
findings. Few of them appreciated the exhaust note at idle & most felt that it is not loud enough to befelt inside the car cabin & expects it to be audible during acceleration which increases the enthusiast ’s
quotient that Indian customers look for.
Table 6 – Sales Volume comparison (Number)
Model

Aug – Dec ‘21

Jan – July ‘22

N Line Muffler

3,196

3,382

About the sales volume of N Line from Table 5, consistently, it is being sold at a volume of 3000
units for 5 months. The Indian market for the sporty economy class is expected to grow further due tothe domination of the young population who prefer excitement & attention-grabbing characteristics.
N Line is a model with appealing interior & exterior, good ride & handling characteristics & mostimportantly sporty muffler notes are expected to grow further if shortcomings in muffler notes fromthe study discussed are considered in future model development.

5. CONCLUSIONSThe Indian customer driving pattern in cities & highways were obtained & were utilized in the
study of exhaust noise performance comparison between N Line & Normal mufflers. According to theresults, the following conclusion can be drawn:(1) To provide a distinguishable sporty characteristic in city driving pattern in 1000 – 2000 rpm
range & a relative difference of peak noise around 3 dB more than the normal muffler.
(2) To keep a noticeable sporty exhaust noise during HGLR condition, which is most used drive
pattern in city conditions.
(3) Offer distinguishable sporty muffler noise especially during acceleration in highway drives,
around 1500 – 3500 rpm, which increase the enthusiastic quotient. A difference of 2 dB morethan the normal muffler in the peak noise level will be appreciated
Above conclusion can be used as design guidelines in the design of sporty muffler note for Indiancustomers to save development time & cost.

REFERENCES
1. Noise and vibration refinement of powertrain systems in vehicles. D.C.Baillie, 2010 Woodhead
Publishing Limited. https://doi.org/10.1533/9781845698041.3.252
2. IS 3028: Automotive Vehicles - Noise Emitted by Moving Vehicles - Method of Measurement by by
Bureau of Indian Standards, 1998 Public.Resource.Org.
3. Population pyramids of India by Sdgedfegw , CC-BY-SA-4.0

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022, in Gyeongju, Korea
Extended abstractABS-0349

Analysis of factors affecting vehicle horn use at urban intersections
Masayuki TAKADA1; Shoki TSUNEKAWA2; Kazuma HASHIMOTO2; Shunsuke YAMASHITA2;
Tamaki INADA3; Yoshinao OEDA4; Ki-Hong KIM5; Katsuya YAMAUCHI6; Shin-ichiro IWAMIYA71,62

Former Graduate school of Design, Kyushu University, Japan
3

Former Faculty of Design, Kyushu University, Japan
4

5

Faculty of Design, Kyushu University, Japan

Faculty of Engineering, Kyushu University, Japan

Faculty of Media and Information Resources, Surugadai University, Japan
7

College of Art, Nihon University, Japan

ABSTRACT
Frequent use of car and motorcycle horns has caused noise pollution in several countries. The final goal of
the present study is to clarify the causes of horn use and to propose countermeasures to suppress horn use insuch areas. To this end, we have been investigating relationships between horn use and factors related to thevehicular environment in Taipei, Taiwan and Seoul, South Korea. We measured and recorded horn use andtraffic circumstances at three intersections in each area. We found the similar result: the frequency of horn
use approximately increased as the traffic volume of standard-sized vehicle. In Taipei, drivers often used theirhorns when they had to wait for the vehicles in front to start moving at the beginning of the green-light phasefor turning left. In Seoul, drivers often used their horn in situations where they turned right as well as in thesimilar situations at the beginning of all green-light phases. In both areas, the mean start delay time when ahorn was used was significantly longer than when a horn was not used. One of the main reasons for horn use,common to both regions, is the delay in starting the vehicle.
Keywords: Vehicle horn use, Acoustic environment, Urban intersection

1. INTRODUCTION
There are some areas facing noise problems caused by horn sounds (1). Horn sounds are emitted
outside the vehicle at a loud enough volume to ensure that drivers inside the vehicle can hear them.
The acoustical properties of the horn sound including volumes are defined by regulations such as theAutomobile Standard Harmony World Forum in 2014 (2). Regulations in various countries also seemto follow this global regulation (3,4). Therefore, as long as the current horn system is maintained, it
is not practical to reduce sound pressure levels to solve the noise problem caused by horn sounds.
Vehicle horn use can be caused by undesirable factors in the traffic environment (e.g., interruptions)(5). Considering these factors, it seems reasonable to identify the causes of vehicle horn use and takecountermeasures to solve the noise problem caused by horn sounds.
In this study, in order to clarify the traffic environmental factors of vehicle horn use such as the
traffic volume and traffic signals, measurements and recordings of horn use and current circumstances
takada@design.kyushu-u.ac.jp
E-mail addresses not disclosed.
3E-mail address not disclosed.
4
oeda@doc.kyushu-u.ac.jp
5
kim.kihong@surugadai.ac.jp
6yamauchi@design.kyushu-u.ac.jp
7
iwamiya@kag.bbiq.jp
12

of the traffic were made at urban intersections in Taipei, Taiwan and Seoul, South Korea. We
investigated relationships between the vehicle horn use and the traffic environmental factors, andcompared the obtained results between two areas.

2. PROCEDURES
Noise measurements and traffic noise recordings were conducted at three urban intersections ineach area of Taipei, Taiwan (6) and Seoul, South Korea. In Taipei, measurements were carried out atthree intersections around Taipei Main Station in October and November 2017. In Seoul,measurements were done at three intersections in the Gangnam District in September and November
2019.
Measurements were conducted continuously for 12 hours (from 7:00 to 19:00) each on one
weekday and one weekend day. A sound level meter (RION NL-52) was used to measure A-weighted
sound pressure level and to record road traffic noise, and several video cameras (SONY HDR-CX560V
and SONY HDR-CX370) were used to capture all vehicles passing through the intersection. For themeasurements in Taipei, the sound level meter and two video cameras were installed on a pedestrian
bridge near each intersection. For the measurements in Seoul, four to six video cameras were placedat three locations at the edge of the road at each intersection, facing in and out of the intersection. The
sound level meter was placed at one of the locations.
Taiwanese traffic regulations include provisions for the use of vehicle horns. In Taiwan, it isacceptable to use the horn in dangerous situations such as emergencies and when entering blind
corners. It is also mandatory to use the horn when overtaking. The horn honking is mainly limited toa short honk. On the other hand, the regulations on the usage of horns can also be found in Koreanroad traffic laws: Drivers are prohibited from making noises that cause damage to others without
justifiable reasons, and are forbidden from repeatedly or continuously sounding the horn. It also
directs the use of turn signals, lights, or horns, as necessary, for the purpose of ensuring safety when
overtaking. In both areas, vehicles drive on the right side of the road.

3. RESULTS
3.1 Traffic Volume
We analyzed the traffic volume using data recorded by the video cameras, and obtained traffic
volume data recorded over 10 minutes for each hourly time zone. For traffic counts, vehicles werecategorized as motorcycles, standard-sized vehicles, and large-sized vehicles. Large-sized vehicles
included large specific vehicles such as buses and truck mixers. Other vehicles (e.g., mini buses, light
Table 1 – Traffic volume for each type of vehicle in Seoul
WeekdayInt. 1
Weekend
WeekdayInt. 2
Weekend
WeekdayInt. 3
WeekendTime Zone
Motorcycle
Standard
Large
Motorcycle
Standard
Large
Motorcycle
Standard
Large
Motorcycle
Standard
Large
Motorcycle
Standard
Large
Motorcycle
Standard
Large

7

8

9

10

11

12

13

14

15

16

17

18

29
756
159
8
310
118
11
447
75
7
131
34
12
522
65
12
177
18

17
870
152
13
515
138
22
589
62
5
193
26
24
675
54
13
225
23

44
778
202
27
729
145
35
645
77
26
419
30
42
639
68
19
414
32

54
778
178
25
805
140
54
697
97
17
403
34
54
640
85
26
451
24

67
803
184
51
847
127
70
680
80
31
572
35
87
625
77
38
487
26

60
681
130
48
880
131
83
604
73
47
527
25
67
587
66
40
488
35

52
869
111
41
933
125
84
622
71
49
650
30
72
657
54
46
615
26

80
1044
122
43
888
134
72
702
68
51
619
22
55
516
39
30
502
20

59
1076
132
40
855
142
61
649
63
35
590
37
68
685
56
47
644
23

70
1052
118
40
944
116
69
677
59
45
554
35
50
511
37
28
508
19

71
1121
129
50
981
115
87
708
45
40
543
21
69
702
46
56
651
30

58
1052
118
32
901
102
73
767
37
48
465
23
63
686
29
36
460
16

trucks) were classified as standard-sized vehicles. In prior to this analysis, we confirmed that thetraffic volume during 1 hour could be estimated from that during 10 minutes for each hourly timezone (6).
Table 1 shows traffic volumes by vehicle type at intersections in Seoul during the measurement
periods. In Taipei, motorcycle traffic was very heavy on weekdays (6). In Seoul, the volume of
motorcycle traffic was very low, and the volume of standard-sized vehicle traffic was the highest
among all vehicle types. Similar to Taipei (6), on weekends in Seoul, the traffic volume of motorcycles
and standard-sized vehicles gradually increased from morning to evening. Traffic volume of large sized vehicles was not so high, but it was always seen during the measurement periods in both Taipei(6) and Seoul.
3.2 Horn Honking
From noise data recorded by the sound level meter, the time at which drivers used their horn wereobtained using data management software (RION AS-60). This data was analyzed within the time
period during which traffic was counted (10 minutes in each hourly time zone).

4. ANALYSIS
4.1 Relationship between vehicle horn use and traffic environment
The relationship between vehicle horn use and traffic volume per lane was examined by using theSeoul data. Similar to the Taipei results (6), a nearly linear relationship was found in Seoul, with ahigher correlation coefficient than that in Taipei (Taipei: r=0.63, p<0.01; Seoul: r=0.70 p<0.01).
4.2 Situations of horn sounds during traffic signal cycle
We investigated the situations in which drivers used their horns during the 10-minute period for
traffic counts. In particular, the relationship between specific timing within the green -light period
allowing traffic in each direction and horn use was analyzed for each intersection. We targetedintersections with four main entrances (exits). Therefore, at each intersection, the traffic signal cycle
consisted of four phases allowing traffic in each direction.
For Taipei data (6), the frequency of honking was counted in each of four green-light phases in the
traffic signal cycle, and was summed up throughout a measurement day. Furthermore, because theduration was different among four green-light phases, the duration of horn counting was equalized to
10 min, and the frequency of honking per 10 min in each of green-light phase was estimated. As aresult, the frequency of honking was high during the green-light phases, which allows left-turn traffic
as well as straight traffic.
Subsequently, to find the causes of vehicle horn use during the green-light phases allowing leftturn (phase II at Intersections 1 and 2 and phase IV at Intersection 3 in Taipei), we analyzed situations
in which drivers used their horns using video data. In this analysis, we first categorized the typicalsituations in which horn honking occurs into four types. Situation 1 is the situation “cutting in line”.
This is a situation where a driver cuts in line to make a left turn and is honked at by another driver.
Situation 2 is the situation “slowness”. This is a situation where a driver driving slowly is honked atby another driver. Situation 3 is the situation “sudden stop”. This is a situation where a driver had tostop suddenly due to the car in front stopping, and use his/her horn. Situation 4 is the situation“delayed departure”. This is a situation when a driver honked his/her horn at another vehicle that was
still stopped at the stop line even though the green-light allowed left-turn. The numbers of these foursituations were counted during the 10-minute period for each hourly time zone.
A similar analysis was conducted on the Seoul data, using 10-minutes video data. This analysis
was performed every 10 seconds during traffic signal cycles within the 10-minute analysis time, taking
into account where horns were used at the intersection. In the case of Seoul, we found six typicalsituations in which horns were used, including “stuck in the right-turn lane,” “indication of intent topass,” “warning of danger” and three types similar to those in Taipei (i.e., “cutting in line,” “slowness,”“delayed departure”).
The result showed that the “delayed departure” situation during the green-light phases allowing
left-turn (Situation 4) was the most common of the four typical horn use at all intersections in Taipei

(6). Table 2 shows horn use frequencies of six typical situations in Seoul. The result revealed that themost common reason for horn use was “stuck in the right-turn lane,” while the second and third mostcommon reason was the “delayed departure” and “slowness”, respectively. At intersections in South
Korea, it is possible to make a right turn even when the traffic signal for going straight ahead is red.
There were frequent situations where drivers of the vehicle behind, who wanted to turn right at theintersection, honked their horns at the driver of the vehicle in front of them who was waiting for the
traffic light to turn green (7). This is one of the reasons why we frequently found horn use in “stuckin the right-turn lane” situations.
Table 2 – The number of situations in which vehicle horn was used at intersections in Seoul
Types of reasons for horn use

Stuck inIndication
Total
DelayedCutting in
Warning
Others Unclear
the rightSlowness of intent
departurelineof danger
turn lane
to pass
Int. 1
Int. 2Int. 3

Weekday
Weekend
Weekday
Weekend
Weekday
Weekend

71
45
91
37
75
52

96
69
106
52
65
57

26
22
18
8
11
4

62
38
29
13
31
23

38
16
29
13
15
14

13
7
10
5
12
13

12
17
10
7
14
12

5
2
3
5
0
0

323
216
296
140
223
175

4.3 Analysis of causes of horn use
In both areas of Taipei and Seoul, we found frequent vehicle horn use in “delayed departure”
situations. To examine whether the vehicle that delayed to start when the traffic signal turned green
induced horn use by drivers of the vehicle behind, the duration from when the traffic signal turned
green until the car at the stop line started moving (the start delay time) was measured . The start delay
times were compared between when a horn was used and when a horn was not used. The start delay
times without honking were significantly shorter than those with honking (p<0.01) and wereapproximately 2 seconds in both areas. The mean start delay time with honking in Seoul was shorter
than that in Taipei (Taipei: 5.2 s, Seoul: 3.3 s).

5. DISCUSSION
In both of Taipei and Seoul, there were almost linear relationships between vehicle horn use andtraffic volume. Although the main vehicle types that accounted for traffic in the two areas differed, it
was supposed that the high traffic volume was one of the factors that might create situations wherehorns were used. On busy roads, dangerous situations are likely to occur.
A micro-analysis using video data was conducted to examine factors related t o vehicle horn use.
Classifying the situations in which the horn was used, we found that vehicle start delay was one of
the major factors of vehicle horn use in both study areas. Therefore, measures to address driverresponse delays to traffic signal may be effective in reducing vehicle horn use. During a red light, thedriver's attention should be kept to direct to the traffic signal. To achieve this, our previous study
proposed to display the time remaining until the traffic signal turns green (6). It would also be effective
to install traffic signals in locations where they are easily visible to drivers.
In Seoul, the most common reason for horn use was “stuck in the right-turn lane”. Drivers areallowed to make a right turn even when the traffic signal for going straight ahead is red. As mentioned
above, it was common to see that drivers of following vehicles who wanted to turn light atintersections honked their horns at drivers of in front of them who were waiting for the light to turngreen in the straight ahead direction (7). Therefore, eliminating this traffic habit (turning right when
the straight-ahead signal is red) may reduce horn use in “stuck in the right-turn lane” situations.

6. CONCLUSIONSThe present study investigated the relationships between the vehicle horn use and the trafficenvironmental factors such as traffic volume and traffic signal cycles. Detailed analysis regarding thesituations of vehicle horn use revealed that vehicle start delay was one of the major factors of vehicle
horn use in both study areas. At urban intersections, the use of horns may be reduced by preventing
delayed departures.
In the near future, next-generation mobility (e.g., self-driving cars) will be widely used in society
and traffic conditions will improve. In such a traffic environment, the current horn system may become
unnecessary. However, as long as the current horn system is maintained, the results of the present
study may help to improve the noise caused by the use of vehicle horns.

ACKNOWLEDGEMENTS
The authors are grateful to Mr. Jong-Hyun Shin for his help with the survey in Seoul. This research
was funded by JSPS KAKENHI grant number JP17H00812.

REFERENCES
1. Phan H. Y. T., Yano T., Phan H. A. T., Nishimura T., Sato T., Hashimoto Y., Community responses to roadtraffic noise in Hanoi and Ho Chi Minh City. Applied Acoustics, 2010; 71(2):107–114.
2. Economic and Social Council of United Nations. Proposal for Amendments to Regulation No. 28;
ECE/TRANS/WP.29/GRB/2014/4; The International Organization of Motor Vehicle Manufacturers(OICA): Paris, France, 2014.
3. Ministry of Environment, Noise and vibration control act, Chap. 4, Arts. 30 and 35, Table 13 (entitled
Vehicle noise acceptance criteria) annexed to Arts. 30 and 35, 2015 (in Korean).
4. Ministry of Land, Infrastructure, Transport and Tourism, Safety standard for road transportation vehicles,
Chap.2, Art. 43, 2016 (in Japanese).
5. Mori Y., Oeda Y., Toi S., Study on the occurrence of horn and traffic environment in a major intersectionin Taipei City. JSTE Journal of Traffic Engineering, 2020; 6(4): A_38–A_47 (in Japanese).
6. Takada M., Tsunekawa S., Hashimoto K., Inada T., Kim K.-H., Oeda Y., Yamauchi K., Iwamiya S.,
Analysis of the effects and causes of driver horn use on the acoustic environment at urban intersectionsin Taiwan. Applied Sciences, 2022; 12:5917.
7. Takada M., Suzuki S., Kim K.-H., Shin J.-H., Iwamiya S., Driver's vehicle horn use and its effects onother drivers and pedestrians: A case study in South Korea, Mechanical Engineering Journal, 2017; 4(2):16-00433.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022 in Gyeongju, Korea

ABS-0517

Improvement of transient vibration of rear wheel drive vehicle byusing elastic axis analysis techniques
HYOUNJIN SIM1; JEONG HUN MOK; BYEONG CHEOL KWAG; SEONG RYONG SHIN
1

NVH Test Team 3, HYUNDAI MOTOR COMPANY, Korea

ABSTRACTIn this study, as a way to improve the starting vibration performance of a new rear-wheel drive platform
vehicle, a development technique using the elastic axis analysis technique at the engine mount position ispresented. Improvements using the proposed technique were applied to the development of vehicles equippedwith rear-wheel drive diesel. The rear wheel drive vehicle has a big difference in the mount support method(front wheel drive: torque support, rear wheel drive: center support) compared to the front wheel drive, andthe engine mount is generally symmetrically applied. In addition, the impact of engine mount on NVH
performance is relatively high compared to front-wheel drive. Therefore, an attempt was made to analyticallyderive a mount improvement plan based on the 2D section-based elastic axis analysis technique at the engine
mount position.
When using the elastic axis analysis technique, analysis and interpretive derivation are possible with basic
information (position, characteristics, inertial specifications, etc.) of the mount system, so it is expected tocontribute to M/H reduction when using the developed CAT technique.
Keywords: Vibration, Elastic-Roll Axis, Rear Wheel Drive Vehicle, Center of Gravity(COG)

1. INTRODUCTION
With the recent increase in the deployment of high-power engines in vehicles and thegeneralization of the Idle Stop & Go (ISG) option, customer demands for the start-up vibration
performance of vehicles continue to increase.

Fig.1 Need NVH performance improvements with ISG application
In particular, for rear-wheel drive platforms adopted by large high-end vehicles requiring quietness,
the P/T mount position for ensuring acceleration transmission sound and R&H performance has been
applied lower than before.
This increased the up/down distance between the engine mount and the powertrain's center ofgravity, which disadvantaged the start-up vibration performance in early development vehicles. The
cause of the start-up vibration in rear-wheel drive vehicles is the phenomenon of vibration according
to the powertrain ROLL direction mode according to the engine torque input and the ductile mode ofthe left/right translational direction. (Fig.3)
To improve the starting vibration, the powertrain rigid body mode frequency must be spaced apartin the mount system design. In this study, we will utilize the Elastic Roll Axis (ERA) analysis
technique of the mount system to improve the starting vibration.
1

shj9438@hyundai.com

5802568 / 10.90.88.152 / 2022-07-15 15:52 (UTC+9) / 본 문서는 현대기아자동차의 사내한 등급 이상의 정보자산이므로 무단 전재 및 복제할 수 없으며, 위반 시 당사 사규 및 관련 법규에 의해 제재될 수 있습니다.

Fig.2 Comparison of startup vibration levelFig.3 Engine mount system & P/T

The elastic axis analysis technique aims to mode decoupling the powertrain rigid body mode bysetting the TRA-ERA separation reduction or the minimization of the ERA-COG distance as acharacteristic value, and selecting the mount position and characteristic value as variables.

Fig.4 Equation of motion of the P/T mount system
To achieve the ideal rigid body mode decoupling, it is possible to match the center of elasticity
with the center of gravity of the powertrain and minimize the ERA-COG distance.

Fig.5 TRA-ERA design for the mount system
To effectively increase NVH performance, an optimization technique from an elastic axis
perspective based on the powertrain Elastic Roll Axis (ERA) is required, which takes into account the5802568 / 10.90.88.152 / 2022-07-15 15:52 (UTC+9) / 본 문서는 현대기아자동차의 사내한 등급 이상의 정보자산이므로 무단 전재 및 복제할 수 없으며, 위반 시 당사 사규 및 관련 법규에 의해 제재될 수 있습니다.

mount position and characteristics simultaneously from the preceding stage. Therefore, in order topredict and improve the starting vibration performance of the rear-wheel drive mount system, a newelastic shaft analysis technique was proposed, and applied to vehicle development through theproposed technique.

2. Elastic axis analysis techniques
2.1 TRA & ERA in rear-wheel drive systems
Rear-wheel drive vehicles have different mount support schemes (front-wheel drive: torque support,
rear-wheel drive: center support) compared to front-wheel drive, and the engine mount is generally
symmetrically applied to the left and right. In addition, the impact of the engine mount on NVH
performance is relatively high compared to all-wheel drive.
In rear-wheel drive vehicles, the start-up vibration phenomenon is caused by the coupling of thepowertrain ROLL direction mode and the left/right translational mode according to the engine torqueinput, and the starting vibration is improved in the design of the mount system in the direction of
separating the powertrain rigid body mode frequency.
Therefore, it is necessary to adjust and set the mount position and characteristic values using the
Elastic Roll Axis (ERA) to separate the powertrain rigid body mode. Torque roll axis is defined as an
axis in which only dynamic rotation occurs when a certain torque is applied to a free rigid body inany direction.
Torque roll axis is the main concept used when setting engine mounting coordinates and rigid bodymodes, and is determined by the powertrain weight distribution at the platform stage, the position of
the powertrain attachment device, and the like. Elastic roll axis is defined as the center axis of rotationby the mount reaction force at the torque input. ERA can be used as a performance predictor during
the vehicle development phase.
By increasing the utilization of CAT based on ERA analysis techniques in the development ofmount performance, the development time and sample cost of the mount can be reduced compared to
the TRA perspective development (mount point).
In rear-wheel drive platforms, the mount support is the center support method, and the engine
mount cannot be matched to the minimum position (the same height as the center of gravity) of the
TRA separation for P/T roll control.
Therefore, the improvement direction should be set through the TRA-ERA separation minimization
method based on the elastic axis analyzer method that takes into account the position andcharacteristics of the mount at the same time other than the TRA and mount separation improvement
method.
2.2 Technique of deriving the center of rotation using spatial vectors
In general, when force is applied to the powertrain rigid body, translational motion and rotationalmotion occur at the same time. To achieve rigid-body mode decoupling under ideal conditions, it ispossible to match the center of elasticity with the center of gravity of the powertrain, minimize the
ERA-COG distance, and match the ERA vector and the TRA vector parallel to each other. However,
due to the structure of the powertrain, it is difficult to match the ERA and TRA 100%.
In particular, in the case of the current elastic axis analysis technique, the COG displacement
derivation technique at the input of the unit torque was used as an indicator for improving the starting
vibration or the low-frequency vibration of the center of gravity.
However, when optimizing mount characteristics, it is difficult to make intuitive judgments on theimportance of each mount and deriving improvements.
Since the COG displacement occurs simultaneously in the upper / lower and left / right of thetechnique utilizing displacement, it is difficult for the evaluator to intuitively judge whether toincrease the mount characteristic of the top or the mount characteristic of the bottom when optimizing
the mount characteristics. (As of Fig.6)
As shown above, when used as an indicator for utilizing ERA analysis and as an elastic pre-central
axis derived from COG displacement, it is easy for evaluators to make intuitive judgments forbalancing mount characteristics.

5802568 / 10.90.88.152 / 2022-07-15 15:52 (UTC+9) / 본 문서는 현대기아자동차의 사내한 등급 이상의 정보자산이므로 무단 전재 및 복제할 수 없으며, 위반 시 당사 사규 및 관련 법규에 의해 제재될 수 있습니다.

Fig.6 Technique of deriving the center of rotation using spatial vectors

Fig.7 Comparison of Technique of deriving the center of rotation
For example, if the elastic rotation axis RC is above the center of gravity (COG), as shown in
Fig.6(b) above, the mount characteristic of the top is lowered in order to lower the elastic rotation
axis, and the mount characteristic of the bottom can be raised by the evaluator intuitively.
Therefore, in this study, space vectors were used to estimate the elastic rotational center position
described above. In order to derive the elastic axis rotation center, the movement before and after thecenter of gravity was used to derive as follows.
2.3 Improved start-up vibration using elastic shaft techniques
2.3.1 Validation of the elastic shaft technique of rear-wheel drive vehicles
Using the techniques derived above, it was applied to improve the starting vibration of the rearwheel drive vehicle and verified. The CAT technique derived from the comparison of the 2nd and 3rd
generation start-up vibration performance of the A car was verified.

5802568 / 10.90.88.152 / 2022-07-15 15:52 (UTC+9) / 본 문서는 현대기아자동차의 사내한 등급 이상의 정보자산이므로 무단 전재 및 복제할 수 없으며, 위반 시 당사 사규 및 관련 법규에 의해 제재될 수 있습니다.

The CAT technique derived from the comparison of the 2nd and 3rd generation start-up vibration
performance of the A car was verified.

Fig.8 Comparison of vibration level for A-CAR 2ND & 3 RD
In the case of the A car, the 2nd generation vehicle shows a smaller distance between the center ofgravity and the elastic rotation center compared to the beginning of the development of the 3rd
generation. (A model 2nd generation: 19.2 A car 3rd generation early: 88.4)
The above results show that the 2nd generation of the A car has an advantage in the starting
vibration. The main cause of the start-up vibration disadvantage in the early 3rd generation vehicles
is the increased separation of the TRA and ERA due to the reduction of the engine mount left/rightspan. Therefore, in order to increase the elastic center of rotation (RC) to improve the vibration at
startup, it is necessary to (1) lower the engine mount left/right characteristic (Y/Z characteristic ratio
downward), (2) apply a torque roll to the top of the P/T, and (3) increase the engine mount position.
(See table. 1)
In practice, the above improvement measures were utilized to improve vibration at the time ofstart-up in the preceding stage of vehicle A and verified by the actual vehicle evaluation. (However,Consideration of Other Performance Table.1(4) is the final application.

Fig.9 Raise the elastic center of rotation (RC) to improve vibration at startup
2.3.2 Utilization of elastic shaft techniques in rear-wheel drive vehicles
In the case of the advanced development stage, the supply of improvement parts is not smooth, so
it is difficult to establish improvement proposals through actual vehicle evaluation. Therefore, when
the developed elastic axis CAT technique is utilized in the preceding stage, efficient development
through simulation is possible. In this study, the above technique was utilized in the development of
the leading stage of the rear-wheel drive diesel-equipped B car. The derived start-up vibration
improvement is a reduction in the amount of elastic rotation center and center of gravity separation
through the position of the engine mount (upward position), and was verified through the evaluationof the actual engine vibration of the B vehicle type. As shown below, the start-up vibration
improvement proposal is an improvement that moves the elastic rotation center upwards, and in thepreceding step, a method of changing the engine mount position, which is the direction of platform
improvement, was derived and approached.

5802568 / 10.90.88.152 / 2022-07-15 15:52 (UTC+9) / 본 문서는 현대기아자동차의 사내한 등급 이상의 정보자산이므로 무단 전재 및 복제할 수 없으며, 위반 시 당사 사규 및 관련 법규에 의해 제재될 수 있습니다.

Fig.10 Simulation results of the elastic center of rotation [RC-COG dis.]The engine mount upward range is 13 mm and 25 mm, so the location of other system components
must also be taken into account. In addition, based on the predicted results, it was confirmed that thevibration at startup when the engine mount position was raised was improved to
1.5 → 1.3 (2WD CASE, 13 mm↑), 1.3 → 1.1.5 (AWD CASE, 13 mm↑).

Fig.11 Simulation of the elastic center of rotation (RC)~COG

3. CONCLUSIONSIn this study, an improved elastic axis analysis technique was proposed to predict the starting
vibration associated with the performance of the rear-wheel drive mount system, and theimprovements obtained through the analytical technique were verified by applying it to the actual
vehicle evaluation. And I came to the following conclusion:① Utilizing the point at which the engine mount of the rear-wheel drive vehicle is locatedsymmetrically to the left / right, an improved elastic axis analysis technique of the 2D crosssectional reference was proposed, and an improved elastic rotation center from the COG
displacement perspective, a previously utilized TRA-ERA performance indicator, was derived
and used as a performance indicator of the starting vibration. This technique is easy fordevelopers to understand when developing powertrain mount system performance and easy tocome up with improvements.
② Through the elastic axis analysis technique developed above, the start-up vibration
improvement plan of the leading stage vehicle was derived and applied to the actual vehicle.
It was confirmed that the predicted improvement plan and the actual difference evaluation
results were equally improved, so that it can be used in the preceding and development stages
when predicting the performance of the mount system.
③ Since the basic information of the mount system (location, characteristics, inertial
specifications, etc.) can be analyzed and derived when using elastic axis analysis techniques,
it is expected to contribute to time and cost savings when utilizing the developed CAT
technique.

REFERENCES
1. Naruse., Analysis of Effects of Power Plant Mount on Shift in Front Engine Front Wheel Drive A/T
Vehicle. MAE Technical Report. No.7.4-12, 1998

5802568 / 10.90.88.152 / 2022-07-15 15:52 (UTC+9) / 본 문서는 현대기아자동차의 사내한 등급 이상의 정보자산이므로 무단 전재 및 복제할 수 없으며, 위반 시 당사 사규 및 관련 법규에 의해 제재될 수 있습니다.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022 in Gyeongju, Korea

ABS-0748

Correlation Study between Suspension System and Idle Vibration
Based on Modal and Sensitivity Analysis
Xianglong Zhang1; Byoung ho Kwon2
1

Hyundai Motor Technology & Engineering Center(China) LTD., China
2

Hyundai Motor Company, Korea

ABSTRACT
Idle vibration usually refers to the steering wheel and seat vibration, which can be felt by the driver when thevehicle stops or traffic jam happens. Traditional solutions related to idle vibration include idle RPM
adjustment and powertrain mode optimization method for source improvement, vehicle mode arrangement
and sensitivity optimization method for transfer path improvement. However, correlation between
suspension system and idle vibration among transfer paths is rarely studied, so this paper proposed a new
direction for improving of vehicle idle vibration by suspension system optimization.
Keywords: NVH, Vibration, Suspension Mode, Sensitivity Analysis

1. INTRODUCTION
Idle vibration performance is the first impression of a vehicle when the consumers make a drive. I n
addition, it is a major factor in determining the luxury of vehicle and also one of the most criticalindexes to evaluate the vehicle comfort performance. For general in -line four cylinder engine, thesecond-order inertia unbalance force generated during combustion is the main reason for idle vibration.
Generally, source of idle vibration generated from PT (Power-train) is finally transmitted to steering
wheel and seat through chassis system and vehicle body. Traditional idle vibration solutions are
typically focus on powertrain source improvement and transfer path improvement(1~3). Optimizationof secondary unbalanced force and idle RPM (Revolutions Per Minute) adjustment are the commonlyused methods for source control, while body mode arrangement, tunning of mount characteristics anda kind of dynamic damper control using FEM (Front-end Module) are the common refinement methods
for transfer path optimization. Anyway, the above methods also have some drawbacks. For example,
idle RPM adjustment requires a compromise for fuel efficiency and air conditioning performance, so it
leads to a limited range for idle RPM adjustment. Ideal powertrain mode and can be achieved byadjusting the mount characteristics, while the R&H and durability performance are easily a ffected.
Higher vehicle body first bending mode and first torsion mode can improve NVH (Noise, vibration andharshness) performance besides idle vibration by adopting reinforced structures on BIW (Body inWhite) which is a high-expense solution. In a word, mode arrangement is a systematic projectrequiring a detailed plan for various performance behaviors which leads to a long time tunning during
the development cycle.
For the suspension system, the impact on R&H (Ride and Handling) performance has a detail ed andin-depth study by now(4~7). However, correlation between suspension system and idle vibration
among transfer paths is rarely studied.
Therefore, a new direction for improving of vehicle idle vibration is proposed by suspension
system optimization in this paper. As a result of correlation study between suspension system and idle
vibration, the reasons of idle vibration for different suspension structures were identified using modaland ODS (Operating Deflection Shapes) analysis methods. Eventually, correlation and sensitivity
parameters that affecting the idle vibration were obtained respectively of different subframe
structures.

1
2zhangxianglong@hyundai-hmtc.com
bhkwon@hyundai.com

2. Correlation Study between Suspension System and Idle Vibration
For the vehicle front suspension system, macpherson type and double wishbone type are commonlyused nowadays. Macpherson type front suspension system has the features including: simple structure,lightweight, low cost and enhanced engine room space, while its motion characte ristics for suspension
system are limited. Characteristics of double wishbone type suspension are opposed to that ofmacpherson type. For the rear suspension system, most vehicles adopt the form of CTBA (Coupled
Torsion Beam Axle) and multi-link type. The overall cost of CTBA suspension is lower than that ofmulti-link suspension, and its overall R&H performance is worse than that of multi -link suspension,
but its road noise performance is relatively easier controlled.

Type A : Basic subframe

Type B : Extended subframe

Figure 1 – Comparison of different subframes
The subframe, as an important part and transfer path on idle vibration of the front suspension
system, is an important component that affects the performance of NVH and R&H. In order to improve
R&H and collision performance, more and more vehicles have adopted the extended structure type, as
shown in the Figure 1 type B, and the mounting type with the body also changed correspondingly.
Subframe of type A is directly mounted to the bottom of the vehicle body, while the subfr ame of type
B is an extended structure that mounted to vehicle body and side member.
Different subframe structures bring out different vibration transfer paths. Figure 2 shows thevibration transfer paths of different subframe structures. For type B, there is an extra vibration transfer
path (subframe-side member-cowl panel- floor and steering wheel) compared with type A whichmakes more difficult on controlling of the idle vibration.
Research Object

Mount

Path 2 (Type B)

EngineMount

Transmision
Mount

Side
MemberDash panel

Strut
Floor or
Steering wheel

KnucklePT
Roll RodSubframe

Lower
Arm

COWL
Panel

Path 1 (Type A and Type B)

Roll Rod

Drive
Shaft

Idle Vibration

Figure 2 – Main transfer paths of different suspension
2.1 Test Setup
Table 1 shows the selected vehicles for study and the vehicle information is also shown in the table.
Since all of the selected vehicles are front-drive vehicles, so the rear suspension system is not the
research object in this paper.

Table 1 – Information of selected vehicles
Item

Vehicle 1

Vehicle 2

Powertrain

1.5T(L4)/8AT

1.5T(L4)/8AT

Powertrain layout

Transverse

Transverse

Drive mode

FF_2WD

FF_2WD

Macpherson type

Macpherson type

Subframe typeType A

Type B

Idle RPM (D Gear & Air conditioner
on)

780 r/min

780 r/min

Font Suspension type

2.2 FEM Effect on Idle Vibration
As described in the paper, tunning of a kind of dynamic damper using FEM is a common method
for transfer path optimization. For verifying the FEM dynamic damper effect of different subfra me
structures. FEM operating frequency of vehicle 1 was tuned from 23 Hz to 27 Hz by adjusting thebush characteristics of the cooling module for approaching the frequency of engine excitation 26 Hz.
Table 2 shows the testing values of idle vibration. It can be viewed that there is a 4 dB improvementof idle vibration through FEM bush tunning of vehicle 1. Then the same experiment was
implemented on vehicle 2 which has an extended subframe (type B). FEM operating frequency of thevehicle 2 was tuned from 22 Hz to 26 Hz to match the frequency of engine excitation 26 Hz, as table
2 shows. Based on the testing results, idle vibration was slightly improved by tunning of the FEM
bush characteristics of vehicle 2, which it works on vehicle 1. This means that the m ethod of FEM
bush tunning can not improve the idle vibration of the vehicle with type B subframe.
Table 2 – Effect of FEM bush tunning on idle vibration
Vehicle 1 with type A subframe

Vehicle 2 with type B subframe

Item
Initial

Tuned

InitialTuned

FEM operating
frequency /Hz

23

27

22

26

Floor vibration /dB

96.1

92.1(▼4.0)

101.2

100.5(▼0.7)

Steering wheel
vibration /dB

106.3

102.1(▼4.2)

108.5

107.8(▼0.7)

2.3 Suspension System Effect on Idle Vibration
Suspension system acts as one of the main transfer paths of idle vibration. Aiming to study thecorrelation between idle vibration and suspension system, the following assumption was made: when
the suspension modes on path 1 coupling with the engine excitation frequency, it will lead to a
deterioration of the idle vibration. By reasonable mode separation between suspension system andengine excitation, idle vibration can be improved. In this paper, suspension system models including
different subframe structures (type A and type B), lower arm, knuckle and st rut were established, as
Figure 3 shows.

StrutStrut

KnuckleknuckleSubframe

Subframe

Lower Arm

Lower Arm(a) Type A wire frame model(b) Type B wire frame model

Figure 3 – Model setup
ODS analysis was implemented on idle condition firstly for distinguish dynamic motion
characteristics of the different wire frame models. The various mode shapes such as pitching, bounce,and bending of the wire frame were exhibited by ODS analysis and the components deflections werealso obtained on the vehicle 1 and vehicle 2 respectively, as table 3 shows.
Table 3 – Summary of running modes
Item

Vehicle 1 with type A subframe

Vehicle 2 with type B subframe

Component

ModeType Deflection

Mode

Type

Deflection

Subframe

Bounce

Rigid

Smal

Bounce

Rigid

Smal

Lower arm

Pitching

Rigid

Large

Pitching

Rigid

Large

KnuckleBounce

Rigid

Large

Bounce

Rigid

Large

Strut

Bounce +Bending

Elastic

Large

Bounce +Bending

Elastic

MediumSince the strut system (subsystem of front suspension system) with type B subframe has arelatively large bouncing deflection. A FRF (Frequency Response Function) experiment which is ameasurement technique to assess the dynamical properties of mechanical components and systems as
a common method aiming to identify the structural dynamics of a structure was conducted on strut
system to verify its natural frequency. For the type B subframe, the results shows that natural
frequency of strut system is 25 Hz which closes the frequency of engine excitation 26 Hz.
Combining the ODS and test results, the reason for large deflection of the strut system with extended
subframe is caused by the resonance between engine excitation and strut system. For changing thefrequency of strut system, improving the stiffness of strut system and adding mass damper on strut
system are the ways often used. While in this paper, changing the characteristics of strut insulator isa more effective way to change the natural frequency of strut system. For obtaining the correlation
between the frequency of engine excitation and natural frequency of strut system, di fferent natural
frequencies of strut system were obtained on vehicle 2. Table 4 represents the characteristics ofvarious strut insulator samples.
Table 4 – Characteristics of strut insulator
Sample（kgf / mm）
Static characteristics
Dynamic characteristics

Ps
Q1
Q2
Pd

Initial
#1
80.2
137.7
209.3
131.1

Sample
#2
61.1
97.3
138.2
85.3

Sample
#3
84.2
143.6
214.8
158.5

P

Q1

Q2

The various strut insulators were analyzed to identify whether the changing of strut systemnatural frequencies may led to an improvement or deterioration on idle vibration. As shown in table
5, when there is a 3 Hz difference between the natural frequency of strut system and the frequency ofengine excitation, the idle floor vibration and idle steering wheel vibration has approxim ately 10 dB
and 7.4 dB respectively. For the vehicle 2 with type B subframe, the strut system has a mode
approaching the frequency of 26 Hz which can be easily coupled with the frequency of engine
excitation. So preventing the strut system mode from coupling with the frequency of engine
excitation is the effective method for the idle vibration improvement of vehicle 2.
Meanwhile, similar experiment was done on vehicle 1 with type A subframe. As table 5 shows,when the strut system mode approaches the frequency of engine excitation, the measured idle
vibration values was not changed, as table 5 shows.
Table 5 – Idle vibration of different Subframes

Item
Natural frequency of strut system /Hz
Floor vibration /dB
Steering wheel vibration/dB
Sensitivity

Vehicle 1 with
type A Subframe
Suspension
InitialOptimization
29
27
96.2(▲0.1)
96.1
105.4(▼0.9)
106.3
Low

Vehicle 2 with
type B Subframe
Suspension
InitialOptimization
25
23
91.2(▼10.0)
101.2
100.1(▼7.4)
108.5
High

(a) Initial _Type A(b) After tuned _Type A(c) Initial _Type B(d) After tuned _Type B

Figure 4 – Comparison of ODS results

According to the analysis results of ODS, deformation of suspension system for vehicle 2 with type
B subframe has been greatly improved. While for vehicle 1 with type A subframe, the deformation has
not changed, as shown in Figure 4.
For a further study, changing the idle vibration transfer paths of vehicle B with extended subframeby cutting the subframe to a structure similar to type A can verify the effect of transfer paths changing
on idle vibration, as Figure 5 shows.

(a) Type B(b) Similar structure to type A

Figure 5 – Subframe rework
Table 6 – Measured idle vibration data

Type B → Similar structure to type A

Item(Vehicle 2)

FEM
Suspension
Optimization Optimization

InitialNatural frequency(FEM/Suspensiom) /Hz

22/25

27/27

22/23

Floor vibration /dB

101.2

95.2(▼6.0)

101.8(▲0.6)

Steering wheel
vibration/dB

108.5

103.4(▼5.1) 107.9(▼0.6)

Sensitivity

-

High

Low

It is observed that vehicle idle vibration with a cutting subframe structure (similar to type A) can
not be improved by strut insulator tunning, indicating that the vehicle with a similar subframe structure
to type A was not sensitive to suspension system while it can be improved by FEM bush adjustment. In
other words, when the subframe structure changes, causing the vibration transfer changes, then thesuspension system sensitivity to idle vibration becomes lower correspondingly, while the FEM
sensitivity is increased.
As table 7 shows, due to the differences on subframe structures, the sensitivity of FEM andsuspension system on idle vibration are also different. FEM sensitivity and suspension systemsuspension sensitivity on idle vibration are summarized as follows.
a. For vehicles with type A subframe, idle vibration is not sensitive to suspension system, while issensitive to FEM bush characteristics, so it can be improved by adjusting the bush of the FEM via a
traditional way.
b. For vehicles with type B subframe, idle vibration is not sensitive to FEM bush characteristics,
while it is sensitive to suspension system. Therefore, idle vibration can be improved by optimizing thesuspension parameters. As an illustration, changing the strut insulator characteristics is a quick
solution to solve idle vibration problem in this paper.
Table 7 – Summary of sensitivity on idle vibration
FEM Optimization
Item

Suspension Optimization

With type A With type B With type A With type B
subframe
subframe subframe
subframe

Effecting on
idle vibration

■

■

■

■

Sensitivity

High

Low

Low

High

Remark■No Effect■ Slightly Improved■Improved(≈5dB) ■ Greatly Improved (≈10dB)

3. CONCLUSIONS
During the development of idle vibration, the effecting of suspension system is often ignored. In
this paper, the idle vibration problem was quickly located by modal and ODS analysis. The causes ofidle vibration occurred were ascertained to provide a reliable basis for modification of the suspension
system by correlation study and sensitivity analysis. Comparative studies were also implemented for
different subframe structures, the parameters that sensitivity to idle vibration were revealed.
Considering the mass production and feasibility in the process of vehicle development, characteristicsof the strut insulator was finally optimized for achieving ideal suspension natural frequency to avoid
the coupling between suspension system and engine excitation. Eventually, significant improvementof idle vibration was achieved which provided a new guiding for idle vibration refinement.

ACKNOWLEDGEMENTS
I would like to express my gratitude to Dr. Byoung ho Kwon, who helped me during the writing of
this paper.

REFERENCES
1. Syed F. Haider, Ahmad Abbas, Francisco Sturla. New Method for Decoupling the Powertrain Roll Modeto Improve Idle Vibration. SAE 01-1588, 2019.
2. Charlie Teng, Ke Zhao, Daiyun Shi, Lianjun Shen. Idle Vibration Robustness Analysis of An I3 Engine
Without Balance Shaft. SAE Technical Paper 01-1023, 2021.
3. Kokaji, J., Komada, M., Takei, M., and Takeda, M. Mechanism of Low Frequency Idling Vibration in
Rear-Wheel Drive Hybrid Vehicle Equipped with THS II. SAE Int. J. Passeng. Cars - 01-2255, 2015.
4. Zhang Yuan-liang, Zhang Li-min.Modal Contribution Analysis of Intercity Rails under Working
Conditions Based on ODS FRF. Noise and Vibration Control Vol 34 No.5, 2014.
5. João Pedro Ruthes, Sérgio Junichi Idehara. Sensitivity analysis of a vehicle suspension system. SAE
36-0021, 2018.
6. Young Min Kim, Ju Young Kim, Jeongkyu Kim. Test Method for Operational Deflection ShapeAnalysis of Squealing Brake Disc in Dynamic Condition. SAE 01-1807, 2012.
7. Pandurangan Muthu Kumar, Manjeet Singh Rammurthy, Manoj Kumar Rajendran. Operational
Deflection Shapes & Resonance Analysis Using Road Simulator. SAE 26-0323, 2019.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022 in Gyeongju, Korea

ABS-0753

Mounting System Refinement for Compatibility development of Ride
Comfort (PT Shake) and NVH Performance

Aoyan Wang1; Wang Xin2; Yunchang Zhang3; Haiteng Sui4
1

Hyundai Motor Technology & Engineering Center(China) LTD., China

ABSTRACT
Ride comfort is known to occupy an important role in the driving performance of vehicles. In particular, PT
shake performance is frequently complained by customers, and the problem is significantly amplified by
the special type of speed bump in China. As a result, the development direction of most automobile
enterprises in China has been become dominated by ride performance. While increased focus on ride
performance has led to more and more conflicts in NVH development, which poses a greater challenging
task to the vehicle performance refinements. This article presents a development strategy that can becompatible with PT shake and NVH performance.
Keywords: NVH, Impact, Vibration, PT Shake

1. INTRODUCTION
It is well known that powertrain mounting system plays an important role in the driving
performance of automobile. It not only support the engine and transmission system, but also areclosely related to the performance of many driving aspects, such as the noise and vibration, the riding
feeling and the durability performance of the vehicle.
With the rapid development of automotive technology, the types of engine mounts and transmission
mounts are becoming more and more diversified. The commonly used powertrain mounts are generally
divided into three types, rubber, hydraulic and active or semi-active control. The choice of mounting
type and the adjustment of characteristics will often affect the vehicle's various performances, and theperformance optimization in a single aspect will inevitably lead to conflicts. The appeal of NVH
engineers is to achieve the purpose of high insulation rate by using the rubber type mounting systemwith lower static characteristics, so as to improve the energy attenuation of powertrain noise andvibration during accelerated driving condition. Meanwhile, static characteristics of mounting also
make a better powertrain mode arrangement to avoid the occurrence of modal resonance in the idle
state. While the development demands of R & H engineers are opposite. In order to ensure good ride
comfort performance, hydraulic mounts with high characteristics are commonly used, especially for
PT shake performance improvement (2,5).
Due to the complexity of the road conditions in China, there are various types of speed bump，and
Chinese consumers are more concerned about on the ride comfort performance, especially PT shake
performance when passing through the speed bump, as shown in Figure 1.

1wangaoyan@hyundai-hmtc.com
wangxin@hyundai-hmtc.com
3zhangyunchang@hyundai-hmtc.com
4suihaiteng@hyundai-hmtc.com
2

(a）Customers' complaints about the ride comfort performance(b）Distribution of customers' complaints of ride comfort

Figure 1 – Online survey of customers' complaints about ride performance (Collected by CVA ~ ‘22.3)
Figure 1 is a online survey on the complaints of Chinese customers about the ride comfort
performance. It can be seen that consumers' attention and complaints about PT shake are relatively
high. The traditional method for improving PT shake is to improve the static characteristics of theengine and transmission mounting. However, this will adversely a ffect the insulation ratio of themounting systems, resulting in the deterioration of NVH performance. Therefore, the compatible
development of PT shake and NVH performance is very necessary (1).
In this paper, the PT shake during the passage of the special speed bump is studied, explores themutual motion relationship between the vehicle body, suspension and the powertrain system throughthe ODS (Operational Deflection Shape) method, and makes a preliminary definition of the specificforms and influencing factors of PT shake. Then, a new concept of the motion phase difference
between the PT and the vehicle body has been introduced, and it is proposed to improve the damping
angle of the mounting system to ensure the compatibility of PT shake and NVH perform ance (4).

2. PT shake phenomenon Analysis
PT shake mechanism was shown in Figure2. When a vehicle passes a speed bump, the energy
generated by the collision between the tire and the speed bump is transferred outward through the hub.
One path is transmitted to the vehicle body through the suspension system, and the other path isdirectly transmitted to the powertrain system through the drive shaft. At the same time, the vibrationenergy transferred to the body was also transferred to the powertrain system thro ugh the mounting
system, these formed the first impact on the powertrain system. After instantaneous impact, due to its
large inertial mass, the engine system will produce continuous shaking under the restriction of the
support to attenuate energy, which forms continuous excitation to the vehicle body and makes
passengers feel a continuous shaking, that is PT shake.

Figure 2 – Mechanism of PT shake
The evaluation criteria of PT shake mainly include subjective evaluation and objective test. In
subjective evaluation, the magnitude and duration of shake vibration are the main evaluation criteria;
while in objective test, the vibration level and decay time of vehicle body are the main reference data
for evaluation.
2.1 Evaluate method setting
In order to accurate define the PT shake phenomenon, two vehicles of the same segment wereselected for the follow-up research after a series of subjective evaluations and objective tests , as shownin table 1. Vehicle A has good PT shake but poor NVH performance such as idle vibration, and vehicle
B has bad PT shake but the NVH performance is quite satisfactory. Through the comparison of thechassis parts of the two vehicles, it is found that the static characteristics of the engine mounting andtransmission mounting of vehicle A are higher than that of vehicle B, which corresponds to the method
mentioned above to improve PT shake by improving the characteristics of the mounting system .
Table 1 – Information of selected vehicles
Item
Powertrain
Layout of powertrain
Engine Mounting
Transmission Mounting
Roll Mounting
Drive mode
Font Suspension typeSub-frame type

Vehicle A
Vehicle B
T(L4)/8AT
2.0L(L4)/6AT
Transverse
Transverse
Hydraulic, Ks 23 Hydraulic, Ks 18
Hydraulic, Ks 28 Hydraulic, Ks 26
Bush, Ks 22
Bush, Ks 23
FF_2WD
FF_2WD
Macpherson type Macpherson type
“口”Type“口”TypeIn order to unify the evaluation condition, it is necessary to select the speed bump type and passing
speed of vehicle. Based on the investigation of the types of speed bumps commonly used, six kinds ofspeed bumps were selected for the evaluation of the experimental conditions, as shown in Figure 3.

Figure 3 – Common used speed bump on Chinese city roads
After the survey of driving habits in Chinese cities, the commonly used speed when passing the
speed bump is generally in the range of 20kph ~ 30kph. Therefore, the speed of 20kph ~ 40kph isselected as the experimental condition in this paper. Then, the comparative test of engine vibration
when passing through different speed bump at different speeds was carried out. The result was shownin Figure 4, the X axis represents the passing speed of vehicle, and the Y axis is the displacement of thepowertrain. It can be concluded that the powertrain displacement is the largest when the vehicle at the
speed of 30kph under No. 4 speed bump. Therefore, No.4 bump was selected for subsequent studies,and passing speed was set to 30kph.

Figure 4 – Bump type and passing speed selection

2.2 System model establishment
In order to intuitive study the PT shake phenomenon, the motion parts of the vehicle involved inpassing the speed bump were simplified into three parts: tire wheel & suspension system, a powertrainsystem and a body system, as shown in Figure 5.

Figure 5 – Main correlation system of PT shake
Referring to the model of simulation research, this paper mainly establishes a quarter car ODS
model for research (6), as shown in Figure 6. The blue structure represents powertrain system; pinkindicates the suspension system, which contains the hub, lower arm and shock absorbers; green
indicates the vehicle body system, it is mainly composed of side member, front floor and seat rails ofdriver and passenger. This model included the vertical mode of the front wheel & suspension system,
the vertical mode of the body and six rigid model of the power train (Bounce, F&A, Lateral, Roll, Pitch,Yaw) (3). Through the ODS analysis of the process of passing through the speed bump in the realstate, the mutual motion relationship of PT, suspension and body system can be obtained.

Figure 6 – ODS Model
2.3 Motion process definition
When the vehicle passes through the speed bump, it can be divided into four processes: front
wheel impact, first aftershock, rear wheel impact and second aftershock. As more than 70% energyof PT shake is caused by front wheel impact, rear wheel impact and secondary aftershock is excludedin this study. Depending on the vibration data of the powertrain and body system, the motion processof PT shake studied in this paper can be divided into two areas, front wheel impact and aftershock
section, as shown in Figure 7.

(a)Powertrain displacement(b)

Mounting case vibration(c)Body vibration

Figure 7 – Motion process analysis
Figure 7 is the comparative analysis of vibration correlation between powertrain system andvehicle body. Figure 7-a is the displacement of the powertrain, which representing the source of PT
shake; Figure 7-b is the vibration of the mounting system; and Figure 7-c is the vibration of vehicle
body, which representing the vibration response in seat rail. It can be seen th at the body vibration in
the aftershock area is mainly due to the powertrain motion. Therefore, the aftershock area is the main
research object of this paper. In order to study the specific motion morphology, it is necessary todefine the influence factors of aftershock area. As shown in Figure 7-c ， by analyzing thetime-domain vibration signal of aftershock region, the motion of this region is divided into three
sub-areas, namely decay time (T1, T2, T3). Each process represents the change of vibration signal
caused by one compression and rebound stroke of the powertrain. Then, the difference between thepeak value and the valley value of each section is defined as the shock level of this region, which
represents the amplitude of PT shake and is named PTP ( peak to peak ) value.
2.4

PT shake analysis of two vehicle

2.4.1 PTP & decay time analysis
Firstly, according to compare the vibration signals of the vehicle body, the PTP and decay time
can be reflected in the measured data, as shown in table 2. It can be seen that the shock level (PTP
values) of two vehicles in the T1 area are almost same. However, after entering the T2 area,
A-vehicle's PTP value is obviously smaller than that of vehicle B. E specially in T3 area, the PTP
value of Vehicle A has been attenuated to a small level, while the Vehicle B is still maintained at alarge level. On the other hand, the vibration attenuation of Vehicle A in T3 area also becomes faster,
resulting in an advantage in the decay time of the whole aftershock area than Vehicle B.

Table 2 – Comparison of PTP and decay time
Item
Vehicle A
Vehicle B

PTP Value
Decay Time
T1
T2
T3
T1+T2+T3
12.2 m/s² 5.8 m/s² 1.9 m/s²
0.22s
11.7 m/s² 9.2 m/s² 5.6 m/s²
0.25s

2.4.2 System deformation analysis
Secondly, through the comparison of the above vibration data, the advantage of PT shake
performance about vehicle A is mainly in T2 and T3 areas. According to the ODS test results, thecomparison of the transient deformation of these two areas was been compared ，as shown in Figure
8. Figure 8 (a) shows the body vibration of two vehicles. It can be seen that in T2 and T3 areas, thepeak value of vehicle A peak is much better than that of vehicle B. Figure. 8 (b) is a comparison ofsystem motion shapes at the second vibration peak in T2 area, dashed line is the initial position of
the model. It can be seen that the body deformation of Vehicle B is worse than vehicle A, and thedeformation of the powertrain is even more. The same situation also occurs at the first vi bration peak
position in T3 area, as shown in Figure 8 (c)(a)

Body vibration analysis(b)

Deformation @ valley area of T2(c)Deformation @ Peak area of T3

Figure 8 – Deformation analysis of two vehicles
It can be seen that the advantages of the A vehicle's subjectively evaluating are mainly due to the
reduction of PTP level, and it is the same trend as the decay time of the powertrain.
2.4.3 Model phase analysis
By comparing PT mode analysis of the two vehicles, specific kinetic forms of both vehicles canbe obtained. It's worth noting that during the aftershock area, the mode phase difference of PTsystem and the body of the two vehicles is distinguished. As shown in Figure 9, black line is thevibration of vehicle body and gray is the vibration of the powertrain. Through the comparison of thetime-domain vibration signals, the phase difference results of the two vehicles can be obtained, as
shown in table 3.

Figure 9 – PT/Body motion phase difference
Table 3 – Comparison of phase difference
Item
Vehicle A
Vehicle B

T1
T1
T1
Compression Rebound Compression Rebound Compression Rebound
170°
150°
170°
90°
160°
30°
180°
180°
170°
150°
130°
90°

In the case of vehicle A, it can be seen that the phase difference between the PT and the vehicle
body begins to decrease during T2 area and get obviously decreasing further after the later trip ,while vehicle B does not change significantly.
2.4.4 Mounting characteristics optimization
In order to reduce the motion phase difference between the engine and the body, the mounting
structure and characteristics of the two vehicles was compared. The results show that the damping
angle of the mounting systems of the two cranes are quite different, as shown in table 4. The
damping angle difference of the engine mounting of two cars is not much different, but the damping
angle of the transmission mounting has a obvious difference. The damping angle of the transmission
mounting of vehicle A reaches 41°, while that of vehicle B is only 23 °.
Table 4 – Comparison of phase difference
Item
Engine Mounting
Transmission Mounting

Vehicle A Vehicle B
32°
34°
41°
23°

To verify the effect of increasing damping angle on PT shake performance, a series ofadjustments about transmission mounting of vehicle B were carried out. Meanwhile, in order toavoid the increase of the dynamic characteristics caused by the excessive increase of the damping
angle, the improvement proposal of damping angle was finally determined to 27° (4).
2.5 Verification of improvement proposal
Finally, the optimized mounting system was used for the real vehicle evaluation of PT shake andassurance evaluation of NVH performance. As shown in table 5, after installing the new mounting
system, the motion phase difference between the powertrain and the vehicle body in T2 and T3
regions decrease obviously. At the same time, the shock level (peak to peak value) in each area isalso significantly reduced, as shown in Figure 10. X axis represents the aftershock area and the Yaxis is the PTP value of vehicle body.

Table 5 – Phase difference of vehicle B
Item
Base
DampingAngle
tuning

T1
T2
T3
Compression Rebound Compression Rebound Compression Rebound
170°
170°
170°
150°
130°
100°
170°

150°

150°

110°

100°

90°

Figure 10 – Improvement effects of vehicle B
Meanwhile, the influence of the new mounting system on NVH performance was also beenverified, and noise and vibration levels such as in idle, acceleration and other aspects did not
deteriorate significantly.

3. Conclusion
This paper defined the exercise process and influencing factors of PT shake relatively clearlythrough the ODS analysis, and suggested the method of studying the motion phase difference
between powertrain and vehicle body. Through the improvement of the mounting system damping
angle, Not only the PT performance is improved, but also the mounting characteristics will notchange too much, thus ensuring the maintenance of NVH performance. This research not only puts
forward a new idea to improve PT shake performance, but also ensures the compatible developmentof NVH performance, effectively improves the conflict between R & H and NVH performance,
which provides guidance for the development of subsequent vehicles and the design of PT mountsystem in the early stage.

REFERENCES
1. Jaehyun Park, Jungmin Hwang, Jaekwon Kim, PH. D., Junghan Lee, PH. D., “Engine mounting system
stiffness optimizes handing, NVH performance”,
2. Autotechreview Aprial, 2016
3. Rong Guo, Jun Gao, and Xiao-kang Wei, “Full Vehicle Dynamic Modeling for Engine Shake with
Hydraulic Engine Mount”, SAE Technical Paper 2017-01-1908
4. Sumit Kakria, Joydeep Chatterjee, “ Effect of Engine Bush Stiffness and Damping on Engine Shake
Performance Using Virtual Simulation (MBD DOE) Approach”, SAE Technical Paper 2019-26-0175
5. Lu-Ean Ooi，Zaidi Mohd Ripin, “Optimization of an engine mounting system with consideration of
frequency dependent stiffness and loss factor”, SAGE JVC Paper 2016-2406-2419
6. Hyeon Gyu Sakong, Gyunchul Hur, Kwang-Joon Kim, Wonju Jeon, “Modal and Forced Response
Analysis of Vehicle Systems With Latent Vibration Modes Due to Hydraulic Mounts”, ASME
2020-041010-1
7. Soliman, A., Moustafa, S., and Shogae, A., “Parameters Affecting Vehicle Ride Comfort Using Half
Vehicle Model,”SAE Technical Paper 2008-01-1146

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022 in Gyeongju, Korea

ABS-0983

Sound modelling techniques for an interactive audio-rendering
simulation of an electric vehicle
Nicolò SALAMONE1-2; Claudio COLANGELI1; Fabio BIANCIARDI1; Mathieu SARRAZIN1; KarlJANSSENS1; Wim DESMET2-3
1

Research & Technology Development Test Division, Siemens Industry Software NV, Belgium
2

KU Leuven, Department of Mechanical Engineering, Belgium
3

DMMS Core lab, Flanders Make, Belgium

ABSTRACTSound simulators are well known tools by NVH engineers for the development and the assessment of soundinside vehicles. To accurately reproduce a target vehicle's sound characteristics in a variety of driving
circumstances, a precise acoustic model of that vehicle is necessary. A first approach consists in recording
in-vehicle overall sound at driver’s ear position for different driving maneuvers and then decompose it foreach acoustic source. Alternatively, using the component based TPA technology, invariant load measurements
can be used to estimate the partial contribution to the in-cabin target without the need of a vehicle prototype.
These two approaches allow the realization of a realistic model that could reproduce the equivalent soundperception at the target position. The combination of the sound modeling and the real-time synthesis
technology enables the user to evaluate the vehicle sound for different driving scenarios while interactingwith the simulator. This paper proposes some innovative solutions for the sound generation anddecomposition process, applied to a battery electric vehicle. The produced model is tested on a NVH vehicle
simulator and assessed through acoustic and psychoacoustic metrics.
Keywords: NVH simulator, sound synthesis, electric vehicles

1. INTRODUCTION
Due to the recent tightening of homologation limits set by governments against pollution emissions,electric vehicles are becoming more prevalent in the passenger vehicle industry. Battery Electric
Vehicles (BEVs) have several advantages over internal combustion engine-powered vehicles,
including zero local pollutant emissions, simplified drivetrains and control scheme s, and lower noise
emissions. However, BEVs offer new NVH challenges both for interior and exterior sound quality.
The e-motor(s), gearbox, and power electronics emit higher frequency tonal components with a low
overall level (1). Despite the low in-cabin noise level, the high frequency noise can be subjectively
perceived as annoying and unpleasant. The absence of the internal combustion engine changes
completely the interior sound perception of the driver. Rolling and aerodynamic noise, as well as
ancillary noises (e.g., HVAC, cooling systems, etc.) become more influent on the perceived soundquality inside the cabin, due to the absence of a main masking source.
Because of the mentioned new challenges, NVH engineers need to reconsider their design criteria– based on Internal Combustion Engine (ICE) vehicles architectures – and investigate creative
solutions. This implies the need to efficiently and quickly assess many design variants and novel
vehicles architectures. Testing all the alternatives and addressing objectively and subjectively all thesalient NVH facets through physical prototypes becomes practically impossible also because of thedrastic contraction of the duration of the modern vehicle development cycles. In this scenario, vehicle
manufacturers are therefore demanding digital approaches for front -loading the full vehicle system1nicolo.salamone@siemens.com

integration and testing through the entire development process avoiding ti me-consuming and costineffective design iterations.
Driving simulators are interactive infrastructures that embed sound generation and auralization
into a multi-physics and interactive environment. One of the goals of NVH simulators is to recreate afaithful representation of the acoustic attributes of a certain target vehicle in a virtual environment
and to do so under different driving conditions. OEMs are increasingly using this technology toforecast the sound quality during the development cycle of new cars, even when a complete prototype
is not available yet (2).
Vehicle sound synthesis techniques ground on the assumption that a car sound can be represented
spectrally as data characterizing broadband and tonal phenomena as a function of vehicle statevariables. These synthesis techniques enable the time domain replication of in -cabin end external
vehicle sound ensuring the sound quality equivalence. The “sound quality equivalence” can be definedas the property of the synthesis to match with minimum tolerance the main sound quality attributes ofthe original version in the same operating conditions (3).
The Simcenter Testlab NVH Simulator is conceived to encode acoustic source breakdown ofexperimental binaural recordings obtained by driving vehicle prototypes under specific conditions(top-down approach), as well as numerical or experimental data retrieved from components tested onbenches or in a simulation environment (bottom-up approach).
The Simcenter Testlab Virtual Prototype Assembly (VPA) is the ideal instrument for generating thedata required for the creation of bottom-up sound synthesis models (4). Component-based TPA is theunderlying methodology used to calculate the sound partial contribution. It involves testing individualcomponents independently and digitally assembling them through the VPA software platform. Fromthe synergistic combination of the VPA and the live synthesis capabilities of the NVH simulator
emerges the benefit of incorporating sound features into tunable auralization models that enable theassessment of many variants of a given vehicle baseline for different driving scenarios.
In this study, a rear-drive electric automobile that has undergone testing on a vehicle rolling bench
is characterized using a combination of the VPA and NVH simulator technologies. After modeling thepowertrain sound with both top-down and bottom-up approaches, the road noise is introduced toprovide a realistic interior sound “ensamble”. The obtained syntheses are compared with the original
sound recordings adopting NVH and sound quality criteria.

2. SOUND MODELING TECHNIQUES
2.1 Binaural sound decomposition and top-down approach
The simplest way to realize sound perceptual analysis is to directly record the in-cabin noise duringexperimental testing if a physical vehicle prototype is available. A top-down approach is here used todecompose tonal and broadband vehicle noise features by running specialized algorithms on
operational data. The extraction of tonal features of rotating machines requires that the mostsignificant orders and their harmonics are detected by listening and inspecting the spectra of thebinaural recording.
2.1.1 Automatic order selection
Currently, the acoustic order selection is mainly a manual and error-prone operation computed by
the NVH engineer. In addition, the spectra of electric vehicle interior noise are even more complex toinvestigate. Although typically far spaced in frequency from one another, EV orders are less noticeable
than in the ICE scenario. To minimize subjective mistakes, automated and adaptive methods for orders
detection based on psychoacoustic norms are being studied.
Figure 1 shows the results of an algorithm that has been developed to automatically detecting theorders that emerge from the background noise. The processed signal is a run-up executed with thestudied vehicle on a roller test bench and controlled in constant torque operation. As a first step, a
tracked spectral processing is executed by using order tracking on the time-domain data. From theobtained rpm-order spectrogram, the proposed algorithm aims to check the prominence by comparing
the amplitude of the analyzed order with an average amplitude of the orders belonging to the same
pre-defined frequency band. The order itself serves as the band's center, and the frequency band's
width is adjusted in accordance with the evolution of the tracked rotational speed and the resulting
frequency. The band widths are obtained by interpolating the centers and the edges of the Bark’s scale(5). The intended order will be chosen for synthesis if its amplitude exceeds a predeterminedthreshold's average. Since the amplitude can change during a speed variation maneuver and since its

value can either reach or not the floor noise for a given vehicle speed, the computation is carried out
at various rotational speeds.
The selected orders are then extracted through order tracking. To accurately track the audio signal
as a function of the motor operational state, a rotational speed sensor is required.

Figure 1 – On the left side the order waterfall of a run-up, on the right side the order detected (marked inyellow) by the automatic order selection algorithm
Broadband noise contributors are typically originated by contact between tires and the road and byinteraction of the wind and the vehicle body. Broadband noise is extracted from the operational
recordings and synthesized adopting the inverse-FFT-based technique described in (6).
2.1.2 Torque mapping
The NVH performance of every vehicle strongly depends on the operating conditions. It is hence
particularly important, when modeling the car sound, to track it as a function of operational quantitiesof the vehicle itself. A correct vehicle sound-operation tracking permits to integrate the analysis of
the NVH attributes into multi-physical digital representation of a studied vehicle. In this view, theNVH model can be coupled with vehicle performance models through co-simulation on a real-time
operating machine (1) by matching the vehicle operation states. The simulation can be performed on
driving platform where the engineer (through the interaction with I/O hardware) can experience a
realistic reproduction of the vehicle and execute a perceptual acoustic analysis.
To have a correct characterization of the acoustic attributes, the vehicle should be tested in its
entire operating range, usually on a roller bench or, alternatively, on test tracks. The motor rotational
speed, the motor load, and the vehicle speed are the three factors that have the biggest impact on anelectric vehicle's acoustics. Other factors, like the gear selected and the state of charge of the battery,affect the operational state by changing the motor speed-load and the throttle-load relationships.
The load is probably the most challenging quantity to measure. Since, torque sensors are intrusive
and expensive for vehicle testing, NVH engineers have learnt to use indirect load indicators like thevehicle acceleration and the accelerator pedal position. In the past, due to the direct relation between
the accelerator pedal position and the opening of the throttle valve in gasoline engines with a drive by-cable configuration, the torque dependency with the accelerator pedal position was robust.
Consequently, NVH tests were usually performed at constant throttle position for simplicity.
Nowadays, the throttle-load relation is no longer dependable due to the advent of electronic control
units, drive-by-wire technology, and, more recently, powertrain electrification. This results in thenecessity to track the acoustics on different quantities that express the actual engine/motor load.
Tracking the power-unit torque can be fundamental in case of architectures that have more than one
machine delivering power, e.g., hybrid-electric vehicles and 4-WD electric drive in a 2-motor
configuration. In this context, the individual torques of the engine and motor(s) are useful to track thesound dependency of each machine. Moreover, tracking the negative torque permits to acousticallyevaluate the powertrain while running in recuperation mode (7).
For the NVH assessment, simple maneuvers are usually performed, such as vehicle accelerations
or decelerations. The vehicle load characterization can be realized by repeating vehicle run -ups atconstant resistant torque, by controlling the roller test bench accordingly. This procedure is carriedout to cover the vastest possible area of the vehicle speed-load operating map. Coupling this strategy
with interpolation techniques enables the simulation of more complex maneuver realized either bydriving the car model freely into the simulation platform or by executing predetermined vehicle cycles.
A focus on mapping acoustic attributes on torque has been realized for the BEV under research.
Tests have been performed on a roller bench and torque delivered by the e-motor have been calculated
considering the vehicle's characteristics and operating quantities. A series of constant torque run-ups
have been executed by controlling the bench in the correspondent mode. For simulating real driving
scenarios, coast-down parameters have been loaded on the roller bench system, simulating the vehicle
force resistance. According to a simplified 1-dimensional dynamic model of the vehicle the torquedelivered by the e-motor is estimated by equation 1.
1 𝑟𝑡𝑖𝑟𝑒Δ𝑉
𝑇𝑒𝑚 =(𝐹 + 𝐹2 𝑉 2 + 𝑚𝑒 )(1)
𝜂𝑔𝑏𝑥 𝜏𝑔𝑏𝑥 0
Δ𝑡where 𝜂𝑔𝑏𝑥 is the gearbox efficiency, 𝑟𝑡𝑖𝑟𝑒 the tire radius, 𝜏𝑔𝑏𝑥 the engaged gear ratio, 𝐹0 and 𝐹2
the coast-down coefficients, 𝑚𝑒 the equivalent mass of the vehicle, and 𝑉 the vehicle speed.
Figure 2 shows the amplitudes evolution of a characteristic order and its harmonic of the tested
powertrain configuration during run-ups. The tests have been executed at different e-motor torqueconditions. The orders under analysis show dependencies to the torque provided by the e-motor.

Figure 2 – Order sections computed for different run-ups executed at different torque levels.
2.2 Component-based modeling and bottom-up approach
The bottom-up approach aims to estimate the sound contribution to the target by assembling
information obtained from components tested experimentally on benches or even numerically from
CAE models. An effective way of building bottom-up NVH models is using Simcenter Testlab VPA,
which allows to create a component-based virtual assembly. All components (either active or passive)and systems that are being developed within the OEM or with suppliers, can be published in VPA as
invariant models.
VPA is based on an evolution of the classical Transfer Path analysis (TPA), called component-based
TPA (C-TPA). C-TPA has the advantage of characterizing the source excitation by a set of equivalentloads (e.g., blocked forces or free velocities) that are intrinsic properties of the active component itself.
In this way, the source component can be modeled independently from the connected rec eiver, and its
behavior can be predicted when coupled with different sub-structures. This approach allows tosimulate structural modifications without having access to complete vehicle prototypes.
For this paper, a rear-axle powertrain of a D segment electric car has been tested and characterizedthrough C-TPA. The vehicle under investigation consists of three different subsystems. The electrical
drive unit (EDU) is the active source, which comprises of the e-motor, the gearbox, and the powerelectronic. The EDU is connected via four rubber bushings to the rear axis carrier (RAC), that, in turn,is linked via four bushings as well to the Trimmed Mass Body (TMB). A schematic of the assemblyis represented in figure 3.

Figure 3: Description of the connections between the e-powertrain and the chassis. Indicators, paths, andtarget positions, used for TPA and ASQ, are specified.

A pair of accelerometers have been instrumented at the connection interface s between the EDU
and the RAC to measure the operational responses on both the active and the passive sides. The source
has been characterized using free velocities (8) on the vehicle assembly assuming limited difference
between free and operational accelerations for the frequency range of the powertrain acoustics in theoperating conditions analyzed. The assumption depends on the dynamic stiffness of the bushings andhas been previously verified for the case under study (9). Under this assumption, the active side
accelerations combined with FRFs of the vehicle subsystems, by using Lagrange-Multiplier
Frequency Based Sub-structuring (LM-FBS) methodology (10), allow deriving the interface forces(or contact forces) at the RAC connections, following the eq.2.
𝐸𝐷𝑈
𝑅𝐴𝐶
𝐹3,𝑖𝑛𝑡 = (𝐻22
+ 𝐻33
+ 𝐾 −1 )−1 𝑎2,𝑓𝑟𝑒𝑒(2)
In case of weakly coupled systems, some matrices can be neglected. This reduces the required dataand can avoid the numerical conditioning issues related to matrix inversion. The engineer can eval uate
the relative importance of the matrices in the coupling equation, depending on the operational
𝐸𝐷𝑈
𝑅𝐴𝐶
condition and frequency range of interest. With the assumptions that 𝐻22+ 𝐻33≪ 𝐾 −1 , it ispossible to rewrite eq.2, obtaining the following eq.3:
𝐹3,𝑖𝑛𝑡 = 𝐾 𝑎2,𝑓𝑟𝑒𝑒(3)
An inverse substructuring approach has been used for identifying the mount stiffness following theapproach reported in (11, 12). The interface forces are coupled with noise transfer functions measured
from RAC to the binaural microphones at the driver position, considered as target. The result is thepower unit structure-borne (SB) noise to the target.
For the airborne (AB) paths identification, the Airborne Source Quantification (ASQ) has beenused. Ten microphones have been placed in the proximity of the powertrain to discretize it in 5
monopole sources and to measure noise radiation during operational tests. To evaluate the contributionof each discretized sources, NTFs towards indicators and the binaural microphones have beenmeasured. Matrix inversion combined with a measured noise radiation from an operational test havebeen applied to retrieve the source strength, as shown in eq.4.
−1(4)𝑄̇𝑗,𝑠𝑐 = (𝑁𝑇𝐹𝑖,𝑗 ) 𝑝𝑖
Finally, the SB and the AB contributions are merged according to the equation 5 to obtain the total
contribution prediction.
𝑛𝑚𝑝𝑇 = ∑ 𝑁𝑇𝐹 𝑇,3 𝐹3,𝑖𝑛𝑡 + ∑ 𝑁𝑇𝐹𝑇,𝑗 𝑄̇𝑗
𝑖=1

(5)

𝑗=1

where 𝐹3,𝑖𝑛𝑡 indicates the forces acting as structural loads, 𝑄̇𝑗 the volume velocities, 𝑁𝑇𝐹 𝑇,3 and𝑁𝑇𝐹𝑇,𝑗 the corresponding noise transfer functions between respectively the interface 3 and thereceiver target 𝑇, and the monopole 𝑗 and the receiver target 𝑇. Once the result contributions to acertain target have been calculated, the total contribution can be synthesized as spectral data.
TPA results are showed in figure 4 where an order section is computed for a motor run-up from1000 to 5000 rpm at the driver’s left ear. The agreement between the component-based contribution(SB + AB) and the operational data is acceptable, with a maximum error under 10 dB.
400
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Pa
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Figure 4: Order sections as a function of the electric motor speed. Total contributions at the driver’s left ear
obtained with C-TPA (SB), ASQ (AB) and compared with angle-domain order tracking of operational data.

3. SOUND SYNTHESISThe NVH simulator's key benefit is the ability to interactively evaluate the NVH comfort byrecreating realistic operating conditions of the vehicle on a driver-in-the-loop interactive environment.
To assess the ability of the NVH simulator of reproduce operating conditions that have not been
directly measured, a speed-load cycle has been performed with the car placed on a roller test bench,
simulating the resistance characteristic of the vehicle. The interior noise has been recorded at thedriver position. Then, the speed and load signals have been used as inputs to simulate the same
sequence of driving maneuvers in the NVH simulator and synthesize the sound at the same target
location. The acoustic model has been created by merging the information coming from 4 vehicle runups executed at different torque levels and a run-down.
An assessment in terms of acoustic and psycho-acoustic metrics has been performed between theoperational test recording and its synthesis, and the results are showed in figures 5 and 6. In this case,the synthesis has been implemented with the background noise and the powertrain noise, both obtainedby the top-down decomposition. The waterfall diagrams of figure 5 display an appreciable result forwhat concerns the overall noise synthesis, while the order cut confirms the synthesis quality in termsof amplitude trends and levels of the key tonal features.

Figure 5 – Spectral analysis and order amplitude of the original binaural recording and its synthesis with a
top-down model. The test performed is a driving cycle executed and simulated on a vehicle roller bench.
In figure 6, a series of acoustic and sound quality metrics are used to assess the acoustic perceptionof the synthesis for the same driving cycle. Overall levels, loudness (ISO 532-1) and sharpness(DIN45692) of the synthesis present high levels of fidelity with the original recording. The roughness(ECMA-418) of the synthesis follows quite well the trend of the original signal except for limited
exceptions when the cycle is more dynamic. Finally, the prominence ratio (ECMA-74) expresses agood match in terms of the tonality of the signals for the most part of the cycle, with a minor mismatchin a high-speed deceleration (from 200 to 220 seconds). Anyway, the error is bounded to 2 dB. The
results can be further improved by increasing the number of tests used for the acoustic model
generation.
An example of a joint application of the VPA and the NVH simulator for a sound perception
evaluation is shown in figure 7. Here, the same driving cycle has been simulated for assessing thestructure-borne obtained with the C-TPA technique illustrated in section 2.2. The two Tone-To-Noise(TTN) ratio maps (ECMA-74) show the difference in the tonal contributions when structure-borne
and airborne together (top waterfall) or structure-borne individually (bottom waterfall) are considered
for the realization of the syntheses. For the total contribution, the powertrain sound has been modeled
through a top-down approach without decomposing the SB and AB contributions. For the sole SBcontribution, a bottom-up model has been realized. Background noise has been considered in theanalysis and added to both acoustic models. This assessment helps to evaluate which path contribution
is tonally predominant and how the diverse driving conditions can affect the result. In the cycle underinvestigation, for example, the airborne mechanism is responsible for the majority of the tonal
contributions, but the structure-borne paths have a strong influence on a particular order at high rpm.

Pa
dB

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acum
Amplitude

asper
Amplitudesone
Amplitude

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Overall level - Synthesis

Y2
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Loudness - Synthesis

Y3
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Roughness - Synthesis

Y4
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Sharpness - Synthesis

Y5
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Prominance Ratio - OriginalProminance Ratio - Synthesis

Y6

Delivered Torque

0.20
0.15
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dB

/2

0.45
6
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Real

3
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Y1
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Figure 6 – Acoustic and sound-quality metrics comparison between the original binaural recording of adriving cycle (red) and its synthesis with a top-down model (blue).

Figure 7 – Tone-To-Noise ratio maps based on the syntheses obtained with two different models, simulatedon the driving cycle: (top) total contribution at the driver’s right ear obtained with a top-down approach,including both structure-borne (SB) and airborne (AB); (bottom) structure-borne contribution modeled
through a bottom-up approach. Both models include background noise.

4. CONCLUSIONSThis paper presents some of the advantages associated with the predictive acoustic modeling ofunder development vehicles for NVH simulation and assessment purposes. The study focalizes on theanalysis of a rear driven electric car. Two sound modeling approaches have been presented. In the topdown approach, the binaural interior noise has been decomposed into powertrain orders and tires
background noise; in the component-based bottom-up approach, the powertrain noise has beencharacterized as of the measurements of the source invariant loads and exploiting C-TPA and ASQ
techniques to retrieve respectively the structure-borne and the airborne contributions. Within theseapproaches, novel techniques have been developed to facilitate the operations of the NVH engineer.
The automatic order selection algorithm speeds up a manual and error-prone process. The torquemapping can facilitate the sound tracking for modern single and multi-power source architectures andincrease the realism of the synthesis. The benefits of the combined use of the VPA-NVH simulator
toolchain have been presented through the synthesis of a driving cycle that simulate operational
conditions not directly tested on bench. The comparison with acoustic and sound quality metrics
returns satisfactory results for a model generated using the top-down approach. Component based
bottom-up approach has been used to estimate the structure-borne contribution: the driver tonal
perception has been simulated and assessed for this specific case. As a next step, since the realizationof a NVH acoustic model as function of multiple operating quantities enables further possibilities interms of subjective analysis, the integration of the NVH model within a multi-physics driving
simulator will be realized in the next future, allowing jury testing in realistic scenarios.

ACKNOWLEDGEMENTS
This paper is partly based on the outcomes of the research project “C-TPA (Component-based
Transfer Path Analysis) – executable digital framework for component based NVH synthesis of e vehicle assemblies” sponsored by VLAIO (Flanders Innovation & Entrepreneurship) and partly
carried out in the Horizon 2020 EU-funded Marie Skłodowska-Curie ITN-ETN training project ECO
DRIVE, https://h2020-ecodrive.eu/, under the grant agreement 858018.

REFERENCES
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2. Mordillat P, Colangeli C, Di Tommaso F, Britte L. Sound Simulator for Hybrid Vehicle NVH
Development. Proc SIA Simulation Numerique; 4 July 2021; digital edition.
3. Adams M, van de Ponseele P. Sound Quality Equivalent Modeling for Virtual Car Sound Synthesis. SAE
technical paper. 2001; 2001-01-1540.
4. Ottaiano S. Master the vehicle NVH performance with a virtual prototype. Blog article; 16 Sept. 2021;
https://blogs.sw.siemens.com/simcenter/vehicle-nvh-prediction-master-the-actual-performance-with-avirtual-prototype/
5. Zwicker E. Subdivision of the audible frequency range into critical bands. J Acoust Soc Am.
1961;33(2):248.
6. Sarrazin M, Colangeli C, Janssens K, van der Auweraer H. Synthesis techniques for wind and tire-roadnoise. Proc Internoise 2013; 15-18 Sept 2013; Innsbruck, Austria.
7. Schecker D, Deisling E, Uerlings P, Estimation of Interior Sound Quality in BEVs Regarding
Measurements on the NVH Chassis Dynamometer: Focusing on Tonal Components. SAE technical paper.
2020; 2020-01-1542
8. Salamone N, Bianciardi F, Colangeli C, Janssens K, Desmet W. A component-based sound synthesis
approach for the subjective NVH assessment of the virtual prototype assembly of an electric vehicle. Proc
ISMA; 12-14 Sept. 2022; Leuven, Belgium.
9. Wagner P, Bianciardi F, Corbeels P, Hülsmann A. High frequency source characterization of an e-motorusing component-based TPA. Proc SIA Automotive NVH Comfort; 13-14 Oct. 2021; Le Mans, France.
10. Linden P, Wyckaert K, van der Auweraer H. Application of a Modular Vehicle Noise and Vibration
Development Approach. SAE Technical Paper. 2001; 2001-26-0037.
11. Meggitt JE, Moorhouse HYA. In situ determination of dynamic stiffness for resilient elements. Proc
Institution of Mechanical Engineers, Part C; J Mechanical Engineering Science, 2015.
12. Haeussler M, Klaassen SW, Rixen DJ. Experimental twelve degree of freedom rubber isolator models foruse in substructuring assemblies; J Sound & Vibration. 2020.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022, in Gyeongju, Korea

ABS-1017

Application of adaptive music vertical resequencing in synthetic
automotive powertrain soundTaylor MAROTTA1; Kyle TAYLOR2
1

Hyundai American Technical Center Inc., United States of America

2Hyundai American Technical Center Inc., United States of America

ABSTRACT
North America has a strong connection with sound in their automotive vehicles. The sound of a car’s
powertrain not only provides a strong emotional connection to the driver, but its sound also gives feedback
about important driving conditions. Electric vehicles lack the same volume and character as traditional
internal combustion engine vehicles, thus the automotive industry has introduced synthetic powertrain sound.
Similarly, driving simulator video games provide synthetic, exaggerated sound as adaptive audio to replicate
racing vehicles for immersion and to provide feedback. In addition to simulated engine sounds, the adaptive
music soundtrack is affected by a player’s indirect actions. In this paper, the adaptive music technique‘vertical resequencing’ is used in place of a vehicles synthetic powertrain sound in order to investigate
important audio connections between the driver and car. By replacing the traditional car like sounds withmusical elements, it will be shown that the correlated changing of the character of a song with verticalresequencing is enough to replicate the driver-car connection synonymous to that of an engine. As shown
successful through studies described in this paper, vertical resequencing can be applied to traditional synthetic
powertrain sound to improve an EV’s appeal and driving experience.
Keywords: Sound, Automotive, Electric Vehicle, Powertrain Sound, Synthetic Sound

1. INTRODUCTION
North American customers have a strong connection with the image portrayed by their automotive
vehicle, much of this is largely influenced by the sound it produces. Due to the overall quiet natureof electric powertrains, the automotive industry is introducing synthetic noises to increase customer
enjoyment, engagement, drivability, and pedestrian safety. Customers have a strong connection withhow their vehicles sound. In 2014, F1 racing changed from naturally aspirated V8 engines totubocharged V6s. As described in (1), there was a significant backlash from fans due to the effects
this can have to the sound of the sport. A similar vocal complaint is also tied to the electrification of
powertrains (2).
Hyundai Motor Group (HMG) produces synthetic powertrain sounds for battery electrified vehicles(BEV) with the Vehicle Enhancement Sound System which produces interior focused synthetic
powertrain sound and exterior focused pedestrian safety sound. This synthetic sound also provides
an identifiable character to the vehicle that is required to represent its brand identity. Certain
concepts, feelings, and music are used to define this brand identity sound, but its execution can bedifficult to achieve while also contributing positively toward drivability and enjoyment. Synthetic
powertrain sounds allow for infinite possibilities, but what the customer is expecting and would enjoy
is largely unknown and ever changing. The current feedback from North American custom ers andmedia is that while silence is appreciated, the perspective on adding engine sound is varied , seen
anywhere from a fun rarely used feature to a necessary feature in performance focused vehicles.
Customers and media are also not shy to identify uninspired synthetic powertrain sounds. In North
America, the sound character provided by some European competitors are held in high regard,
providing a curated experience and useful feedback to the driver that are not seen a s a gimmick. For
example, HMG’s BEV synthetic powertrain sound customization options for and muting capability
12

tmarotta@hatci.com
ktaylor@hatci.com

are greatly praised, so if a sound can be created where the customer enjoys it enough to leave it
activated for a majority of driving, a vehicle’s value can be greatly improved.

2. ADAPTIVE MUSIC TECHNIQUES
2.1 Engaging Audio
With an internal combustion engine (ICE) vehicle, the driver receives feedback from all senses,
which provides a fulfilling driving experience. The strong attachment toward automotive sounds isalso key for audio design in racing simulation video games. Racing games are required to simulate
all feedback though audio, visuals, and controller vibration. This simulated feedback is often
exaggerated to compensate those missing experiences. The sounds created are not entirely authentic
but are a cinematically authentic aka “cine-real” representation to better meet player expectations (1).
Similarly to real driving, these games also relay vehicle performance information to the player though
the simulated engine audio. Game designers take great effort in creating an engaging audio
experience for players by incorporating real vehicle audio and physics based simulations (1).
Electrified vehicles lack the full sound experience of an ICE thus rely on synthetic powertrain soundin the same way as racing video games do (Figure 1). Synthetic powertrain sound and racing video
game sound design have the same goal to provide feedback of vehicles operation and make theirdriving experience more enjoyable.

Figure 1 – Synthetic powertrain sound is used to replace the missing feedback senses expected in
ICE vehicles. Racing video games similarly replace physical stimuli with synthetic in an
exaggerated, cine-real fashion.
Video games are a unique entertainment media such that events and actions are not linear as they
are dictated by the player’s variable inputs. This mostly requires games to have what is known as
dynamic audio, in which the player’s indirect and direct action affect the audio elements of the game(3). Adaptive audio, including soundtrack in the form of adaptive music, are affected by the player ’s
indirect actions such as their health status, the time of day, and position in a race leaderboard.
Adaptive music enhances the player’s engagement while also providing subtle feedback to theiractions as well as extends the player’s tolerance to the length of a song. A simple example of adaptive
music is in Taito’s 1976 “Space Invaders,” where the tempo increase as enemies get closer to theplayer. Over the past decades, adaptive music has become more complex and integrated into manydifferent variables. In Turn 10 Studios’ 2015 automotive racing game “Forza Motorsport 5”, themusic will change based on your position on the track, leaderboard position, and d uring exciting
events such as passing another racer. This was done not only to keep the player engaged, but to also
allow the soundtrack to remain fresh after multiple races on the same track (4).
2.2 Resequencing Techniques
Adaptive music is often composed with several discrete musical assets defined as stems that are
typically organized in either horizontal or vertical resequencing (5). Horizontal resequencing
arranges the stems in a dynamic sequence where they can be looped or re -arranged based on dynamic
variables (Figure 2a). Vertical resequencing arranges the stem in layers in which multiple stems canbe played concurrently (Figure 2b). The dynamic variables dictate when each stem is played. This
paper focuses on the incorporation of vertical resequencing into a synthetic powertrain sound, thoughhorizontal resequencing could be utilized as well to enhance the driving experience.

Figure 2 – a) Horizontal Resequencing, b) Vertical Resequencing
In adaptive music, stems can be made from entire songs to only a few notes from a single
instrument. The key is that the stems are seamlessly interchangeable so that they do not create
unintentional tonal or rhythmic dissonance. Short transitional stems such as stings or fills may be
used to assist in a dramatic change between larger stems. In PlatinumGames’ 2017 “Nier Automata”,
nearly the entire soundtrack was adaptive with each track capable of portraying a different emotional
responses, in most cases a sense of urgency (6). Alternatively in Hello Games’ 2016 “No Man’s Sky”,
the developers hired the band 65daysofstatic to create a bespoke album loosely based on the design
elements of the game. The developers made stems from small elements of the albu m and tied eachto specific gameplay variables. Each stem was often only a single instrument over a few measures.
The result was procedurally generated soundtrack that could never be replicated twice (7).

3. ADAPTIVE MUSIC VEHICLE APPLICATION
3.1 Creation and Evaluation
The changing instrumentation, rhythm, and theming of adaptive music replace engine like sounds
to link drivetrain performance to driver emotional experience. To demonstrate and evaluate theeffectiveness of adaptive music synthetic powertrain sound, music stems from video games were used
to build several designs to be evaluated in a VI-grade NVH Desktop Simulator. The simulator
accurately replicates automotive noises and is equipped with a steering wheel and pedals, allowing toa driver full control of a vehicle on a simulated road track. The main objectives of the evaluations
were 1) to determine if adaptive music alone can communicate powertrain dynamics to the driver as
an enhancement to natural powertrain sounds and 2) to determine the most effective inputs to changesound character in order to build a proper synthetic powertrain sound. The looping adaptive musicstem files extracted from video game’s soundtracks were used as sound assets for the synthetic
powertrain sound. The vertical stems were transitioned between each other with crossfading.
To determine the effectiveness of the music techniques applied to synthetic powertrain sound,
different designs were evaluated by Hyundai Motor Group vehicle dynamics test engineers.
Participants were asked to drive a simulated battery electric vehicle in the VI-grade NVH Simulator
with a selection of synthetic powertrain sound designs. They were then prompted with aquestionnaire to quantify their driving experience. Figure 3 provides an example of the driver’s
visualization while operating the simulator.

Figure 3 – VI-grade NVH Simulator visualizes during a powertrain sound evaluation study
Vehicle speed and motor load were the two inputs chosen to control the synthetic power train sounddynamics. The more traditional synthetic powertrain sound input of throttle position was replaced

with motor load as it gives a more natural connection between the vehicle and sound. The throttle
position input can erroneously be made to feel like the pedal is directly controlling the synthetic
powertrain sound instead of the vehicle’s own physical powertrain components.
The simulated vehicle and track were designed to encourage the driver into different dynamic
conditions including a winding country road and highway onramp merging. The participants hadcomplete control over the accelerator, brake and steering wheel. Allowing the participants to steer
was believed to be essential in order to keep them immersed with the vehicles driving dynamics whichwere a focus of the study. A simulated electric vehicle’s wind, road, and natural motor noises without
synthetic powertrain sound was used as a baseline reference for participants. For the synthetic
powertrain sound configurations, the simulated reproduction of the vehicle’s natural driving sounds
was played along in order to better simulate a drivers experience while also using the synthetic
powertrain sounds.
3.2 Initial Study
The goal of this initial study was to determine if a simple vertical resampling of two stems froman adaptive music track can provide sufficient driver feedback of a vehicle’s dynamics as well as
determine which of the two driver inputs, motor load and vehicle/motor speed, is more sensitive tomusical intensity. This synthetic sound was designed to only crossfade between two stems ofdifferent intensity and to change sound amplitude. The driver inputs, motor load and vehicle/motor
speed independently controlled the musical intensity and sound ampli tude. As visualized in Figure
4, two versions of the synthetic powertrain sound were created by essentially swapping the driver
inputs. By comparing the participant’s responses between the two versions, the driver input
sensitivity to musical intensity could be determined.

Figure 4 – The two versions tested in the first study looked at the connections between driver
inputs and musical intensity.
Instead of simply changing the volume gain to adjust sound amplitude, an adaptive low pass filter
was used. Simple experimentation indicated that a listener of music like sounds, unlike time constantsounds, could not easily detect small changes in amplitude gain. By linking a driver input to thecutoff frequency of low pass filter, an illusion of the sound source moving closer or further away wascreated. This effect enhanced the dynamic sensitivity more than amplitude gain while having thesame perception of a louder or quieter sound.
The vertical resequencing was assembled as a crossfade between two stems from the track “Fodlan
Winds” extracted from the 2019 Nintendo video game “Fire Emblem Three Houses”. Most of thegame’s soundtrack was arranged with two distinct stems: a low intensity version for the overhead map
where slow paced tactical gameplay occurs and a high intensity version used during close ups ofcharacters fighting. The intensity of the music is determined by the arrangement of instrumentation,rhythm, and accentuation. Because the track is required to transition at any time based on the player’s
action, the tempo and structure of the tracks stems are identical and well suited to this experiment.
Participants were asked to drive the simulator through three laps of a track. During the first lap,
the vehicle only produced the simulated natural sounds of a battery electric vehicle. This not only
provided the driver a mental baseline to judge the performance of the adaptive music synthetic
powertrain sounds, it also provided an opportunity for the participant to become familiar with theoperation of the simulator and its track. Upon completing the first lap, Version A of the synthetic
powertrain sound was activated, and for the final lap, Version B replaced Version A. In thequestionnaire, participants were asked to evaluate relative to their experience during the first lap
which was without the added synthetic powertrain sound.
The results of questionnaire of the first study indicated that both versions of the adaptive music
synthetic powertrain sounds enhance the participant’s driving experience, particularly in the
categories of driver connectivity to vehicle dynamics and how the sound encourages more spirited
driving. Version A, with the music intensity tied to the motor load, outperformed Version B in
intended design. Participants felt that Version A gave a better indication of speed and connection to
the vehicles driving dynamics.
To the participants in the study, sound character dynamics and a stronger connection to motor
load were key to making a powertrain sound that enhanced their experience. This actually seems
clear when one considers that in an ICE, the driver experiences a drastic character change thoughphysical engine sounds such as induction noise and change in RPM dynamics through shifting. The
spectral and time character of the natural ICE sound is directly controlled by the throttle input directedby the driver. This is opposed to several production synthetic powertrain sounds designed for BEVs.
Often they are designed such that pedal input increases sound amplitude and vehicle speed changes
sound character only through pitch shifting.
3.3 Complex Multi-Stem StudyAs the first study’s results concluded that the vertical resequencing music to create a synthetic
powertrain sound was successful in enhancing the driving experience, a second study was derived to
explore if an effective sound can be designed with only vertical resequenced stems. Sound amplitudechange would be limited to only to help transition between stem changes.
To create this new synthetic powertrain sound, PlatinumGames’ 2017 Nier Automata track “CityRuins” was used as it contained multiple stems. In the game, the stems are used to reflect the player ’s
dynamic experiences as they explore an area. The track’s single vocal and three instrumental stems
were used to build a synthetic powertrain sound. The ambient instrumental stem was put through a
low and high pass filter to create two unique sounds to allow for additional stems to create the dynamic
range of the powertrain sound. The low pass filtered ambient stem provided a calm emotional feelthat was played at idle and while the vehicle was stopped, while the high pass filtered ambient stem
had a futuristic shimmering sound that mixed well with high speed driving. The vocal stem wasplaced so that it would play during the most aggressive driving situations, similar to how the game
would play it during its more engaging moments. The stems were arranged, as in Figure 5, tocrossfade between each other in order to provide more excitement when the vehicle is driven
aggressively while also providing the driver important performance feedback. This synthetic
powertrain sound was labeled as Version C in the study. To determine if this could further enhancethe driver’s experience, an altered Version A was assembled. This Version D replaces Fire Emblem’s
stems with the same Neir Automata’s stems in Version C (Figure 6). Stems were mixed together suchthat they retained the same musical elements of Version C, but its vertical resequencing dynamics
were simplified into two crossfaded stems.

Figure 5 – Version C was designed such the vertically resequenced stems are a function of bothvehicle speed and motor load.

Figure 6– Version D was designed identically to Version A but used combined stems from
Version D.
Evaluations of Version C and D were performed similarly as Version A and B. With the VI -grade
NVH Simulator, participants drove a BEV equipped with the two version of the simulated powertrainsounds. Like in the first study, the first lap was driven with the vehicle only playing natural simulated
BEV sounds to provide a reference for the participants to evaluate the target powertrain sounds.
Their opinions were recorded with a questionnaire given out during their driver session.
The results of this study indicate that participants preferred the exclusively vertical resequenced
synthetic powertrain sound, Version C. The added layers of complexity were appreciated with thevehicle sound feeling more dynamic and engaging in different driving conditions. The addedcomplexity also was successful in providing more detailed and precise driver feedback. The detailed
aspects of designing Version D was more difficult and time consuming, which led to some negative
comments regarding the percipients desired preferences. These comments were not related to thevertical resequenced aspect of the sound and could be addressed with additional resources.

4. Conclusion
Adaptive music vertical resequencing was successfully incorporated into the design of a synthetic
powertrain sound to enhance the driver to vehicle connection experience. By isolating sounds tochange only musical intensity as a replacement for conventional ICE type sounds, it was shown thatcharacter change, not sound amplitude, is the most important connection to driving performance,
particularly when linked to motor load. Though an electric motor does get louder with increased loadand speed, driver’s perception is dominated by the character change (pitch time and spectral character)and less so by the sound amplitude. Changing character can give a more detailed, almost
subconscious, clues on the performance of the vehicle’s powertrain. Adding additional layers andcomplexity to the synthetic powertrain sound enhances the driver’s experience with the only known
tradeoff of increasing design manpower costs.
For a more traditional sounding powertrain sound, the musical stems can be replaced with more
expected non-musical sounds. As visualized in Figure 7, common production synthetic powertrainsounds are design with one or more motor synchronous sounds that rely only on pitch shifting andamplitude changes to affect the sound dynamics. A proposed synthetic powertrain sound withvertical resequencing would look much like Figure 8. Multiple motor synchronous and non synchronous sounds would each have multiple vertically resequenced stems. Drivers are likely to
enjoy and engage with the feature more as it is more complex, natural, alive, and provides clearer
performance feedback. Horizontal resequencing should also be investigated as it has the opportunity
to provide sound events during certain driving conditions such as a rapid pe dal input or regen brake
limits, thus furthering the ability of synthetic powertrain sound in providing driver with their desiredconnected driving experience.

Figure 7– Common visualization of a synthetic powertrain sound.

Figure 8– Visualization of a proposed engaging synthetic powertrain sound.

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PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022, in Gyeongju, Korea

ABS-1019

An experimental investigation into the vehicle ventilation system
and its effect on wind noise
Kyle TAYLOR1; Michael CSAKAN2
1

Hyundai American Technical Center Inc., United States of America

2Hyundai American Technical Center Inc., United States of America

ABSTRACT
Traditional approaches to improving wind leak noise have usually involved improving vehicle
sealing system performance and changing exterior air flow around the vehicle. This paper proposes
an additional consideration for improving leak noise: reduce the pressure difference across the seal
by lowering cabin pressure. The vehicle ventilation system’s effect on cabin pressure wasinvestigated including the intake, HVAC system, and extraction system. It is shown that the first andmost important step is to improve the extraction system. This is done by lowering the exteriorpressure near the extractor through controlling aerodynamics and designing an unrestricted flow pathfor cabin air to exit the cabin via the extractors. Then the intake / HVAC system should be tuned
such that it provides sufficient fresh air flow for defogging and passenger comfort, while preventing
excessive flow at higher vehicle speed. Targets should be set for cabin pressure and key factors that
affect cabin pressure.
Keywords: Wind Noise, Leak Noise, HVAC, Body Extraction, Automotive

1. INTRODUCTION
Traditional approaches to improving wind leak noise have usually involved improving the vehicle’s
sealing system and controlling the exterior aerodynamic flow around the vehicle. This paper
proposes that another useful method to improve leak noise is to reduce the pressure difference acrossa seal by lowering vehicle cabin pressure.
First, the wind leak noise mechanism and relationship to pressure is shown. Throughbenchmarking several vehicles’ ventilation systems, key factors related to cabin pressure are identified
and relationships are validated through vehicle testing. Finally, the cabin pressure’s effect on windleak noise is confirmed.

2. BACKGROUND
2.1 Leak Noise Mechanism and Relationship to Pressure
Leak noise occurs due to air passing through a weak seal, usually from the interior to the exterior
due to unsteady aerodynamic flow pressures (1). Figure 1 is an example of the leak noise mechanismof a window seal. It is logical that greater pressure difference (p = p2-p1) across a weak seal cause
greater air leak and usually louder leak noise. This effect was confirmed by a simple test where windnoise was measured while decreasing pressure across a seal by applying wind noise repair techniques(Figure 2).

12ktaylor@hatci.com
mcsakan@hatci.com

Figure 1 – Simple leak noise model

Figure 2 – Wind leak noise effect of pressure difference across a weak seal.
The leak noise model in Figure 1 suggests several approaches for improving leak noise:
1. Improve the sealing system.
2. Change exterior aerodynamic flow to increase p2 which reduces the p across the seal.
3. Change the cabin ventilation system to decrease p1 which reduces the p across the seal.
The most common solution for a leak noise problem is to improve the sealing system. However,if this approach is used without consideration of the pressures acting on the seal, the sealing system
can be sensitive to production variation. For robust wind noise performance, careful considerationof all 3 approaches is required. The focus of this paper is the investigation into approach #3, reducing
the cabin pressure, which is often overlooked.
2.2 Explanation of the Vehicle Ventilation System
Sufficient fresh airflow is required through the vehicle cabin for defogging and passenger comfort,
and vehicles are equipped with intake and extraction systems to provide vehicle ventilation.
It can be useful to visualize the cabin pressure as the sum of 3 effects (Figure 3).
1. The fresh air entering through the intake system tends to raise cabin pressure. This
includes the HVAC case and blower. This system is both passive (ventilation due topressures from vehicle speed) and active (ventilation due to blower).
2. Cabin air exiting through the body extraction system tends to lower the cabin pressure. Ina passive system, the exhaust of air from the body while the vehicle is in motion depends
on the negative pressure (suction) at the extraction location, usually under the rear bumper,and the ease of which air can pass through restrictions, such as interior trim panels, theextractor grill, and the bumper.
3. Uncontrolled body leakage, for example: leakage past seals, can raise or lower the cabinpressure depending on the aerodynamic pressures around the vehicle at the leak location.
In this paper, only the controlled vehicle ventilation system was studied: the intake and extraction
system operating together. The uncontrolled body leakage was not studied.

Figure 3 – Vehicle Cabin Ventilation System Model
The controlled ventilation system is under study consisting of 1. intake and 2. extraction systems
The intake and extraction systems work in harmony to provide vehicle ventilation. Consider two
different hypothetical vehicles which achieve the same cabin fresh air flow, and have the same sealing
weaknesses. One vehicle relies mainly on the intake system to achieve cabin ventilation and theother primarily on the extraction system. Figure 4 and Figure 5 depict these vehicles.

Figure 4 – Intake DominantFigure 5 – Extraction Dominant

The intake dominant vehicle has a rough underbody and aerodynamics which result in minimal
suction at the body extractors. The body extractor is small, with tight clearances to body panels, andnoise treatment is placed in the air flow path, all of which results in significant extraction flow
restriction. Because of the poor extraction system performance, the intake system must push in fresh
air excessively to achieve sufficient air flow. This results in higher cabin pressure.
The extraction dominant vehicle has a smooth underbody with high speed airflow, theaerodynamics create very low pressure region near the extractor grill. There is an unrestrictedairflow extraction path with large clearances, and noise absorbing treatments are carefully designed
to minimize airflow restriction. The extraction system does most of the work of pulling air throughthe cabin, which results in a relatively lower cabin pressure.

3. VEHICLE BENCHMARKING RESULTS
3.1 Vehicles
4 vehicles were included in this study, with the following characteristics:
Sedan A: good leak noise performance and lower cabin pressure.
Sedan B: poor leak noise performance and higher cabin pressure.
CUV C: good leak noise performance and exceptionally low cabin pressure.
CUV D: poor leak noise performance and higher cabin pressure.
3.2 Test Method
Measurements were made according to Figure 6 at various vehicle speeds and HVAC blower
settings. Additional measurements were made with the fresh air intake and extraction system blocked(taped) which is used as a reference. Environmental wind conditions have a strong effect on the
results.
To minimize this effect, testing was conducted with wind less than 3m/s andheadwind/tailwind measurements were averaged.

Figure 6 – Measurement points
Some of the measurement points need clarification:
1. Cabin p+, cabin pressure change (increase) due to the vehicle’s ventilation system operation:
This is the cabin pressure measured during the test case minus the cabin pressure measured
with the intake and extraction system blocked at the same vehicle speed. This value can bethought of as “how much the ventilation system changes the cabin pressure”. Although it ispossible for this number to be negative, it is positive for every test case in this study.
2. Extraction system flow capability: This is a measurement of the extraction system’s
capability to allow air to flow out. A higher number means that air can easily flow out(unrestricted), a lower number means that the path is restricted. It is the volumetric flow rate
through the extraction system divided by the pressure difference across the extraction system.
It is measured during a static test with the vehicle cabin held to a specific pressure difference.
All measurements presented in this paper are normalized to a percentage of the maximum measured
in the 4 vehicles.
3.3

Cabin Pressure Benchmarking Result

In Table 1 the poor noise performance vehicles (Sedan B, CUV D) have much higher cabin pressure
increase from the ventilation system (cabin p+) than the good noise performance vehicles (Sedan A,
CUV C).
Although several vehicle speeds and HVAC settings were checked, fresh blower at setting 3 at
highway speed was selected as a standard for two reasons:
1. It tends to be the worst case for leak noise subjectively (blower speeds above setting 3 are too
noisy).
2. This test condition includes all of the vehicle ventilation factors investigated in this paper.
Table 1 – Cabin Pressure Result

3.4

Extraction System Benchmarking Result

Due to aerodynamics properties, all four vehicles tested have negative pressure near the extractor(pext ). Lower pressure along with an unrestricted air path from the cabin through the extraction path,helps to reduce cabin pressure. Together, these factors define the passive extraction performance
while the vehicle is in motion.
In Table 2, the sedans have similar pext , but Sedan A has better extraction system flow capability.

For the CUVs, CUV C has much lower (better) p ext and better extraction system flow capability than
CUV D. The extraction system performance of CUV C is exceptionally good. It is notable that
CUVs tend to have lower under bumper pressure than the sedans, likely related to their aerodynamics.
Table 2 – Extraction system results

3.5 Validation of Exterior Pressure Near the Extractor Effect on Cabin Pressure
CUV D was selected to confirm the relationship between aerodynamics, exterior pressure near the
extractor, and cabin pressure. CUV D was modified as depicted in Figure 7: 1. Additional
underbody panels. 2. Removal of the front air dam lip. 3. Added rear spoiler. Table 3 shows thatthe aerodynamic modifications applied to “CUV D Modified Aero” significantly lowered the pressurenear the extractor which in turn lowered the cabin pressure.
Table 3 – Extractor pressure relationship to cabin pressure

Figure 7
3.6 Validation of Extraction System Flow Capability Effect on Cabin Pressure
Sedan B was selected to confirm the relationship between improved extractor air flow and cabinpressure. Interior trim and body extractors were modified to allow significantly more air flow
through the extractors.
In Table 4, the increase in extraction flow capability reduces cabin p+ significantly. Additionally,
there is a noticeable improvement in wind leak noise and fresh air entering through the HVAC inlet
was increased by 6%.
Table 4 – Extraction system flow capability relationship to cabin pressure

3.7

Intake Fresh Air Flow Benchmarking Result

Fresh air flow (Q int ) entering the cabin via the intake system will tend to raise the cabin pressure.
The intake system should provide sufficient fresh air flow for defogging and passenger comfort, but
it should not be higher than necessary. How much the cabin pressure increases for a given flow is afunction of the extraction system performance.
The total fresh air flow entering through the intake system is the combined passive (vehicle speedrelated) and active (blower) contributions. It can be useful to consider the passive and active
contributions separately by measuring the passive contribution at high speed with the blower off andthe active contribution stopped with the blower on.
High pressure near the intake inlet (p int ) will increase the passive air intake effect of the vehicle athigh speed and will increase cabin pressure. Some passive air intake is beneficial for the HVAC
system, but to minimize the cabin pressure effect, it should not be excessive. Therefore, p int should
be considered carefully and not simply maximized.
In Table 5 Sedan B has slightly less passive fresh air intake flow and more active fresh air flowfrom blower operation than Sedan A. The cabin p+ is increased significantly, which suggests thatthe extraction system needs improvement. Compared to CUV C, CUV D has higher fresh air intake
flow, and very high cabin pressure increase. This suggests that CUV D may have excessive in takesystem flow (both passive and active), while also needing extraction system improvement.
Table 5 – Intake system results

3.8 Validation of Fresh Air Flow Effect on Cabin Pressure“Sedan B with increased extraction flow” was selected to confirm the effect of reducing intake
fresh air flow, and a restriction was installed in the HVAC inlet system. Table 6 shows that the
restriction in the HVAC inlet system reduces cabin p+. The added restriction also reduced fresh air
inlet flow as expected. When modifying the intake system, there is a trade-off between fresh air flowrate and cabin pressure. It is notable that although Sedan B with both modifications has similar intake
flow and a better extraction system, there is still more cabin pressure increase from HVAC operation(cabin p+) when compared to Sedan A. This is related to the aerodynamic flow pressures anduncontrolled leak paths which are beyond the scope of this study.
Table 6 – Fresh air flow relationship to cabin pressure

3.9 Validation Exterior Pressure Near the Air Intake Inlet Effect on Cabin Pressure
CUV D was selected to confirm the relationship between aerodynamics, exterior pressure at the air

intake inlet and cabin pressure. CUV D was modified with a hood deflector as shown in Figure 8
and Table 7 shows the test result. The aerodynamic modifications applied to “CUV D with hooddeflector” lowered the cowl pressure to approximately the same as CUV C, and as expected thislowered the passive fresh air pressure effect on cabin p+. In this test, the fresh air inlet flow was notmeasured directly, but the reduction in pressure difference suggests fresh air inlet flow was also
reduced.
Table 7 – Intake inlet pressure relationship to cabin pressure

Figure 8
3.10 Detailed Characterization of the Total Vehicle Ventilation System
It can be beneficial to characterize the entire ventilation system by measuring fresh air intake flow
(Q int ) and cabin pressure change (cabin p+) for different vehicle speeds and blower settings. As an
example, CUV C and CUV D are compared in Figure 9. The total passive ventilation effect (intake
and extraction systems combined) is shown for 130kph with a red box, and the active blower effect at
130kph with a yellow wedge. In Figure 9a, CUV C appears to have reasonable fresh air flow fromboth passive and active contributions. In Figure 9b, CUV C has excellent cabin pressure performance,in particular there is almost no increase in cabin p+ with increasing vehicle speed. This indicatesthat the ventilation system balances the cabin pressure with exterior aerodynamic flow pressures. In
Figure 9c and 9d, CUV D has higher fresh air flow and much higher cabin p+. This s uggests that
there may be excessive fresh air flow from both passive and active ventilation and that the extraction
system performance is poor.

Figure 9– Vehicle ventilation system characterization

4. WIND NOISE VALIDATION
Sedan B with the cabin pressure modifications described in sections 3.6 and 3.8 was selected for awind noise test to demonstrate the relationship between leak noise and cabin pressure. The 4 windnoise test cases are:Case 1: Initial condition, fresh, blower level 3 (cabin p+, 40)Case 2: Improved extraction capability, fresh, blower level 3 (cabin p+, 30)Case 3: Improved extraction capability and HVAC restriction, fresh, blower 3 (cabin p+, 23)Case 4: Initial condition, Recirculation mode, blower level 3 (cabin p+, 0)Figure 10 shows cabin p+ at different blower speeds for each of the cases at 130kph and Figure 11
shows the wind noise test result in fresh mode blower 3 at 130kph. This vehicle has a severe leaknoise issue and the improvement is obvious.

Figure 10 – cabin p+ for each test case

Figure 11 – cabin p+ effect on leak noise

5. CONCLUSION
Although the sealing system and exterior aerodynamic pressures are critical areas to consider forwind leak noise, the cabin pressure also has an influence on leak noise and is often not well controlled.
For wind leak noise robustness, measurement and control of the cabin pressure is advantageous.
Targets for cabin pressure and the contributing factors should be set via careful collaboration withother teams, such as HVAC, Aerodynamics, NVH, and Packaging.
The first and most important step to reduce the cabin pressure effect from the ventilation system
is to improve the body extraction system. This is done by lowering exterior pressure near the
extractor through aerodynamics and designing an unrestricted flow path for cabin air to exit the cabin
via the extractors. Then the intake / HVAC system should be tuned to provide sufficient fresh air
flow for defogging and passenger comfort without excessive flow, especially at higher speed. In
particular, care should be taken so that the speed induced passive fresh air intake effect is controlled
and not simply maximized.
While an unrestricted extraction flow path for exiting cabin air is critical for minimizing the cabinpressure, the extraction system must also isolate noise entering the cabin through the body extractors.
This requires careful consideration while designing components such as extractors, bumpers, interior
trim panels, the package tray, and noise isolation treatments.

REFERENCES
1. Watkins S. Aerodynamic noise and its refinement in vehicles. In: Vehicle Noise and Vibration Refinement.
Elsevier; 2010. p. 219–34.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022, in Gyeongju, Korea
Extended abstractABS-0521

Design of Micro-Perforated Panel Absorber for Highway Noise
Lee Woong-yong1
1

Korea Expressway Corporation Research Institute, Korea

ABSTRACTMicro-perforated panel absorber(MPPA) reduced the target noise without the sound material such as grasswool and can be made by a variety of material. The MPP considering the noise characteristics can be designed
to controlling the hole diameter, the porosity, the panel thickness, and the cavity depth. In this paper, the
MPPA is designed for highway noise with high noise level in the 1 kHz band. The designed MPPA has multilayer because of narrow bandwidth of the conventional MPPA. Considering the difficulty and cost of
production, it has 0.5 to 0.7mm of the hole diameter, 1 to 5% of the porosity, the panel thickness less than
1mm, and cavity depth 50mm. The sound absorption band of the double-layer MPPA is 435 to 2125Hz andthis band exceeds absorption coefficient 0.5. The double-layer MPPA has a flat peak in 720Hz to 1355Hz.
Keywords: Micro-perforated panel, Highway noise

1. INTRODUCTION
Micro-perforated panel absorber(MPPA) can reduce the noise in the target frequency band by
adjusting the design factors (hole diameter, panel thickness, porosity and cavity depth) without using
a sound absorbing material.1) It can be manufactured from various materials and is used in various
fields such as aircraft and automobiles. Traditional single-layer MPPA has a narrow sound absorbing
band. In order to improve the weakness, the MPPs are arranged in multiple layer or in parallel.
In this paper, MPPA without the sound absorbing material is applied to highway noise. the doublelayer MPP is designed for highway noise characteristics. The MPPA is designed as a double-layer that
can absorb the main frequency band of highway noise.

2. DESIGN OF MICRO-PERFORATED PANEL ABSORBER
Highway noise characteristics are different depending on the type of pavement, but a peak appears
in the 1kHz band. Considering the manufacturing difficulty and overall thickness, the design factorsof MPPA are limited. For the random noise, the sound absorption coefficient of MPPA is calculatedby the transfer matrix method. Table 1 shows the specification of MPPA. Figure 1 is a cross-sectionalview of MPPA.
Figure 2 shows comparisons between the absorption coefficient of MPPA and the noise level ofconcrete pavement measured by OBSI (On-Board Sound Intensity) method. Absorption coefficient of
the designed MPPA is more than 0.5 in 435Hz to 2125Hz. MPPA has a flat peak in 720 to 1335Hz.
The absorption band of MPPA includes the main frequency band of highway noise.
Table 1. Specification of MPPA

1

Diameter(mm)Thickness(mm)Porosity(%)

Cavity depth(mm)Up-layer

0.7

0.7

5

50

Down-layer

0.5

0.7

1

50

knulp@ex.co.kr

Figure 1. The cross-sectional view

Figure 2. Comparisons between absorption coefficientof MPPA and noise level

3. CONCULSION
In this paper, a double-layer MPPA for highway noise is designed. The MPPA has a soundabsorption band with an absorption coefficient of 0.5 or more in 435 to 2125Hz. The absorption bandof the MPPA covers the main frequency of highway noise.

REFERENCES
1. Maa D.Y. Theory and Design of Micro-perforated sound absorbing constructions. Sci. Sin.1978; 17:5571.
2. Maa D.Y. Micoperforated-Panel Wideband Absorbers. Noise control Eng. J. 1987; 29:77-84.
3. Yairi M, Sakagami K, Takebayashi K, Morimoto M. Excess sound absorption at normal incidence by two
micoperforated panel absorbers with different impedance. Acous. Sci. & Tch. 2011. 32(5);194-200.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022, in Gyeongju, Korea
Extended abstractABS-0725

Comparisons of tire-pavement noise characteristics between
longitudinal tining and diamond grinding concrete pavement
Woong-yong Lee1; Chul-whan Kim2
1,2

Korea Expressway Corporation Research Institute, Korea

ABSTRACT
Concrete and asphalt pavements are widely applied in Korea. Concrete pavement has advantages of durability
and low maintenance cost. Disadvantage of concrete pavement is a higher noise level than asphalt. To
improve it, longitudinal tining (L.T) and diamond grinding (D.G) methods apply to concrete pavement. This
paper analyzes the noise characteristics of highway to which two pavement techniques were applied. Tirepavement noise is measured by OBSI system. Test was conducted a total of 12 times in the first lane. The
vehicle speed is 100±10km/h. The average noise level of L.T is 104.4dB(A) and D.G is 104.7dB(A). In
frequency analysis, there is no significant difference in noise level. Both pavements have two peaks between
800 and 2000Hz.
Keywords: Tire-pavement noise, OBSI

1. INTRODUCTION
Various types of pavements are applied to reduce highway noise. Concrete pavement is widely
applied in Korea because of its durability and low maintenance cost. However, the noise level ofconcrete pavement is larger than asphalt pavement. To improve the shortcoming, longitudinal tining(L.T) and diamond grinding (D.G) are applied to the concrete pavement. In this paper, the acoustic
characteristics of the pavements are compared and analyzed after measuring the pavement noise withOBSI (On-Board Sound Intensity) method.

2. TEST AND ANALYSIS
2.1 Pavement
The main purpose of L.T and D.G is to improve improving ride. Additionally, the noise reduction
is confirmed. be included in the extended abstract. L.T improves the surface of the concrete pavement
at the beginning of construction. D.G enhances the pavement surface of the degraded concrete with adiamond blade installed grinder.
2.2 OBSI method
Tire-pavement noise is measured by CPX (Close-Proximity) method using a specially designed
trailer. OBIS method without trailer measures the acoustic power per unit area at point near the tire pavement interface.
2.3 Measurement
As shown Figure 2, test device is installed on the left rear wheel of the test vehicle. The speed of
the test vehicle is 100±10 km/h. Noise is measured 12 times on the first lane in the same section.
Measurement data are analyzed according to the type of pavement and the vehicle traveling direction .

12

knulp@ex.co.kr
c.h.kim@ ex.co.kr

Figure 1. Comparisons between longitudinaltining and diamond grinding pavement

Figure 2. Test device on the test vehicle

2.4 Results
The average noise level of L.T is 104.4dB(A) and D.G is 104.7dB(A). Figure 3 shows that thenoise characteristics between the two pavements are similar. Both pavements have two peaks between
800 and 2000Hz. There is no significant difference in the comparison of the overall level according
to the vehicle traveling direction. It is confirmed that this result is similar to the results of otherresearchers.

Figure 3. Comparisons between L.T and D.G

Figure 4. Comparisons according to the direction

3. CONCLUSION
The noise of concrete pavement applied with longitudinal tining and diamond grinding is measured
using OBSI method and analyzed. It is confirmed that there is no significant difference between L.T
and D.G in the frequency and overall level.

REFERENCES
1. AASHTO Designation TP 76-15. Standard Method of Test for Measurement of Tire/Pavement Noise
Using the On-Board Sound Intensity (OBSI) Method. 2015.
2. Hong S.H, Han S.W. Noise reduction of aged concrete pavement using diamond grinding method (InKorean). Journal of the Korean Society of Road Engineers 2006;8(4):30-36.
3. Mun S.H., Lee K.H., Cho D.S.. Software Development of the Traffic Noise Prediction Based on the
Frictional Interaction between Pavement Surface and Tire. Journal of the Korean Society of Road
Engineers 2011;13(2): p.67-75.
4. Washington State Department of Transportation. Performance of Concrete Pavements with Longitudinaltining, Transverse Tining, and Carpet Drag Finish. 2012.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022 in Gyeongju, Korea

ABS-0882

Proposals of a Refined Road Traffic Noise Assessment
Methodology for Hong Kong
C.K. CHIU1; C.K. LAU1, C.C. LAI1, Aaron S.W. LUI2, Chris K.T. KWOK2,Matthew K.H. CHEUNG2, C.K. LEE2
1

Ove Arup & Partners Hong Kong Limited, China, Hong Kong

2

Environmental Protection Department, Hong Kong SAR, China, Hong Kong

ABSTRACT
Hong Kong is a compact city and road traffic noise is a prevalent environmental problem to be tackled as apriority. The unique spatial settings, including extensive application of roadside cantilever noise barriersand enclosures, street canyon, high-rise buildings, flyover structures, extensive use of Low Noise Road
Surfacing materials and innovative noise mitigation design (e.g. acoustic windows, balconies), etc. have
imposed specific challenges for road traffic noise modelling for residential planning applications,
Environmental Impact Assessment studies, Strategic Environmental Assessment studies, etc.
The Environmental Protection Department (EPD) of Hong Kong Special Administration Region (HKSAR)
has initiated a study to review the widely adopted road traffic noise assessment methodologies viz.
CNOSSOS-EU, FHWA TNM and CRTN, and to recommend appropriate methodologies for modelling theunique complicated situations for Hong Kong. The study has examined the suitable assessment approaches
for source characteristics, source locations, source terms for different vehicle categories, provision for newenergy vehicles, multiple reflections and diffractions, propagation over elevated structures and noise barriers,
reduction by different road surfaces and innovative noise mitigation design, etc. Field measurements are
conducted to verify key assumptions and methodologies for local application. A pragmatic approach to
implement the assessment methodologies into a digital platform will be recommended.
Key words: CNOSSOS-EU, FHWA TNM, CRTN, CRTN-HK

1. INTRODUCTION
The “UK Calculation of Road Traffic Noise” (CRTN) has been adopted in Road Traffic Noise
Impact Assessments (RTNIA) for planning applications, statutory Environmental Impact Assessments(EIA), Strategic Environmental Assessment studies (SEA), etc. in Hong Kong, and is a reliable
methodology for prediction of road traffic noise for most common situations.
In Hong Kong, the landform and spatial settings are very complex, in particular the metro anddeveloped areas are crisscrossed by a road network of various at-grade roads, flyovers, tunnels andunderpass structures; and streets / roads in old districts are flanked by dense and high-rise buildings
on both sides, creating a street canyon environment. All these circumstances are challenges for moreaccurate noise predictions. To tackle traffic noise impacts, EPD of HKSAR has been implementing
a combination of effective at-source noise mitigation measures, including road-side noise barriers,
enclosures, Low Noise Road Surfacing (LNRS), and at-receiver mitigation measures. In view of thelimitations of conventional LNRS on local road application due to its low speed, frequent vehicle
braking at junctions and pedestrian crossings, inclined roads and sharp turnings etc, significant efforts
have been made by EPD and Highways Department to explore a more durable LNRS materials for
local road application. More recently, the use of at-receiver Innovative Noise Mitigation Measures(1) (INMDs) such as specially designed acoustic windows and acoustic balconies have been widelyadopted in many new residential developments. The use of INMDs can effectively reduce the noisetransmitted into residential units while allowing natural ventilation, and hence can effectively address
the traffic noise issues in a compact urban environment.
The Government of HKSAR is also actively implementing measures to aim for achieving zerocarbon emissions before 2050 under its “Climate Action Plan 2050” (2) which includes thepopularization of Electric Vehicles (EVs). Statistics suggest that the total registration of EVs in Hong
Kong has increased significantly during the last decade. As at the end of June 2022, the total numberof EVs is 33214, representing about 3.6% of the total number of vehicles in Hong Kong (3). Other
than electric private cars, a major bus operator has commissioned the first specially designed electric
double-decker bus. To promote the use of EVs, the Government and private developers have beenproactively installing the EV charging facility. As at the end of June 2022, there are 5,046 EV
chargers for public use including 2,746 medium chargers and 877 quick chargers in Hong Kong (3).
It is anticipated that this trend for electrification would accelerate in coming years, and the road traffic
noise exposure in Hong Kong would be progressively improved.
As none of the common international road traffic noise assessment methodologies can provide
specific procedures to simulate the unique and complicated settings for a compacted city like Hong
Kong and to address the latest developments of innovative noise mitigation measures such as the newform of LNRS and INMDs, as well as the future popularization of EVs, there is a genuine need toreview and propose an extension to or modification of the current methodology. While the project“Refined Road Traffic Noise Assessment Methodology for Hong Kong” (RTNAM-HK) is still ongoing,
it is expected that the outcome of this modification would provide specific procedures for better
treatment on source geometry, traffic dataset available, noise emission datasets, provision of EVs,
noise propagation, special noise reduction of INMD etc.

2. REVIEW OF NOISE SOURCE TERM
Other than CRTN, Common Noise Assessment Methods in Europe (CNOSSOS-EU) and Federal
Highway Administration Traffic Noise Model (FHWA TNM) are also widely adopted in various
countries. A review has therefore been conducted to compare these methodologies and identify the
major aspects that may better be modified and new features that could be introduced to better suit theHong Kong situations.
2.1 Vehicle Classification
The vehicle classification system in CRTN only contains 2 vehicle categories as light and heavy
vehicles, while both CNOSSOS-EU and FHWA TNM contain at least 4 vehicle categories. While all
the categories for the 3 methodologies are defined according to the respective laden or unladen weightof vehicles, none of these definitions matches the vehicle classification in Hong Kong which has atotal of 10 categories according to Annual Traffic Census. While the vehicle classification in CRTN
is the most straightforward amongst the 3 methodologies, a more refined vehicle classification could
better fit the Hong Kong vehicle classification and enhance the accuracy of noise prediction, andhence optimises the need and extent of noise mitigation measures. As the Government of HKSAR ispromoting EVs, additional open categories for EVs shall therefore be considered for RTNAM-HK.
2.2 Source Configurations
In both CRTN and FHWA TNM, roads are modelled as multiple line sources while roads in
CNOSSOS-EU are represented by point sources. While using road segments in CRTN is generally
applicable in Hong Kong, in order to optimise the calculation for complicated situations, such as
diffraction from cantilever barriers, short barriers/ enclosures with gaps in-between, multiple
reflections from elevated barriers or structures and portal opening, etc., it is recommended to divide
the traffic on road section into a series of short segments of 1m long and each represented by a point
source in RTNAM-HK. This would also enable a more accurate prediction of reflections anddiffractions and hence optimise noise mitigation measures.
2.3 Source Lines
The traffic noise source in CRTN is simulated by a single source line positioned at 3.5m from thenearside carriageway, excluding hard shoulders etc. In case the two carriageways are separated bymore than 5m or where the heights of the outer edges of the two carriageways differ by more than 1m,
the noise level produced by each of the two carriageways shall be evaluated separate ly.
In CNOSSOS-EU and FHWA TNM, each lane is represented by a source line at the centre of thelane. To reduce the number of source lines, CNOSSUS-EU allows one source line to be placed on
the outer lanes of each carriageway. In the case of a two-way road, source line could be placed atthe middle of the road (see Figure 1).

Figure 1 – Illustration of placing source lines in 3 assessment methodology
As a left-hand traffic city, vehicles in Hong Kong shall keep to the left most lane of an expressway
unless heading for a right-side exit or overtaking vehicles. Medium goods vehicles, heavy goods
vehicles and buses are also prohibited from using the right most lane of an expressway where thecarriageway has three or more traffic lanes available unless the y are heading for a right-side exit.
However, according to Chatterjee et al. (4), drivers on a multi-lane highway tend to choose a
specific lane based on their desired speed, vehicular composition of traffic flow, presence of accesspoints, road surface condition and vehicle performance, etc. Thus, the distribution of flow among
traffic lanes is not homogeneous among lanes. Besides, according to Wu (5), drivers travel on the
median lane rather than on the shoulder lane during high volume of traffic. Observations in Hong
Kong also match these findings. As such, the distribution of traffic flow among lanes depends ondrivers’ preference on various factors and has no regular pattern.
In order to investigate a suitable approach for handling multiple lanes in Hong Kong, a sensitivity
test has been conducted for a straight section of Tolo Highway, at which the traffic flow was 6,270
veh/hr, 22% of heavy goods vehicles and an average speed of 100kph. The sensitivity test has
adopted CNOSSUS-EU to cover 3 scenarios with the source line modelled at (i) each lane; (ii) outerlane; and (iii) middle of road, and at 10m and 20m from road kerb, and at various elevations. The
SPL has also been converted from L eq to L 10 by a factor of +3dB(A).

Figure 2 – Sensitivity Test using Tolo Highway as an Illustration
Results in Figure 2 reveal that for scenarios (i) and (ii), the difference in SPL at 10m and 20m fromroad kerb at an elevation of 4m is about 1.5 dB(A) and 1.0 dB(A) respectively. This difference
diminishes as elevation increases. For scenarios (i) and (iii), the difference in SPL at 10m and 20m
from road kerb at an elevation of 4m is very small, with only about 0.3 dB(A) and 0.1 dB(A)respectively. The difference in SPL also diminishes when elevation increases.
The sensitivity test has also considered the screening effects caused by a typical 4m high roadsidevertical barrier for scenarios (i) and (iii). Results indicate that the difference in SPL at elevations
well within both the illuminated and shadow zones are small, in the order of less than 1dB(A), andthe difference increases to few dB(A) within mid-way elevations, depending on whether the sourceline could be screened by the barrier. If scenario (ii) is adopted, the difference in SPL within midway elevations would increase, and could be more than 10dB(A).
Moreover, the GIS road data set, which is now freely available from Government of HKSAR, hasbeen based on a single line placed in the middle of each carriageway. As such, this data set would berecommended for use as the basis of road data for RTNAM-HK to facilitate RTNIA by local

environmental consultants. For general RTNIA applications, it is considered appropriate to adopt thesource line at the centre of each carriageway. For a more detailed/accurate assessment due to thepresence of roadside noise barriers with receivers at certain distance and height from the road, ascenario source line placed at individual lanes could be considered.
2.4 Rolling Noise and Propulsion NoiseAmong the 3 methodologies, only CNOSSOS-EU considers rolling and propulsion noise separately
while the others consider the aggregated noise based on vehicle mix, travelling speed etc.
Calculating rolling and propulsion noise as in CNOSSUS-EU allows the flexibility to consider EVs
which there is no engine noise while rolling noise would remain similar as vehicles with internal
combustion engines. RTNAM-HK should therefore consider the contributions from rolling andpropulsion noise separately. Nevertheless, according to studies (6) (7) (8), the CNOSSOS-EU
appears to give an underestimated sound power coefficients for the noise emission and critical review
is required to reflect local vehicle characteristics and circumstances. As such, separate field noise
measurements are being conducted to establish the local specific source terms of different types of
on-road vehicles and a robust data set of vehicle noise emission will be generalized for incorporation
into the RTNAM-HK. Further, a methodology to map the vehicular classification in Hong Kong to
CNOSSOS-EU is also required. For example, Light Goods Vehicles in Hong Kong have a laden
weight of less than 5.5t and should be apportioned to Light Motor Vehicles (laden weight <3.5t) andMedium Heavy Vehicle (laden weight >3.5t) in order that RTNAM-HK would follow the vehicle
classification in Hong Kong. It is however considered appropriate to conduct continuous review tofine tune the noise source terms to better suit the new and different categories of vehicles in HongKong.
2.5 Road Surface and Age of Road Surface
Noise barriers and LNRS are typical solutions to tackle the road traffic noise problem in Hong
Kong (9). While noise barriers generally provide better noise reduction performance for screened
receivers, they occupy more spaces than LNRS. Besides, LNRS could benefit over a wider area thannoise barriers and avoid secondary impacts associated with noise barriers, such as blockage of view
and increased shade (10).
Among the 3 methodologies, CRTN and CNOSSOS-EU include correction for different roadsurfaces, while CNOSSOS-EU also considers age effect on road surface. EPD and Highways
Department of HKSAR have conducted extensive tests on Polymer modified friction course (PMFC)
and Polymer modified stone mastic asphalt of 6mm nominal maximum aggregate size (PMSMA6) for
both local roads and high speed roads to ascertain suitable correction factors. Results reveal that
newly laid PMFC has an average Roadside Noise Reduction (RS reduction) of 4.6dB(A) at high-speed
roads (with brushed concrete as reference road surface), while the measurement and monitoring resultsover a 30-month monitoring period showed that the average RS reduction of were 2.6dB(A) for
resurfacing with PMFC and 2.8dB(A) for resurfacing with PMSMA6 at local roads (with wearing
course or bituminous surface as reference road surface). According to records, RS reduction can be
maintained before road surfaces deteriorated for resurfacing works as the RS reduction after 2.5 years
for both road types are similar to that when newly laid. Therefore, age of road surface is considered
not a significant issue and a correction of 2.5dB(A) for LNRS on both high speed roads and local
roads is proposed.
2.6 Road Gradient
CRTN considers the effect of road gradients for upward direction only. CNOSSOS -EU considers
the effect of gradients for both upward and downward directions for propulsion noise emission but
there is no gradient correction for powered two-wheelers. FHWA TNM considers the effect ofgradients for upward direction for heavy trucks only. It depends on the decelerating speed for theroad segment which requires a detailed breakdown of speed for heavy trucks. However, for traffic
forecast in Hong Kong, traffic speed is normally predicted for the entire road link and the traffic
stream instead of breaking down to each road segment and each vehicle type. Hence, there arelimitations to consider the effect of gradient as in FHWA TNM.
According to the Road Users’ Code in HKSAR, it is advised to select and stay in a low gear whendriving downhill and propulsion noise in 1 st gear reveals the highest noise emission (11). Thus, it isconsidered appropriate to customize RTNAM-HK to consider both uphill and downhill corrections.

2.7 Acceleration and Deceleration
CRTN does not consider the effect from acceleration and deceleration of vehicles, while both
CNOSSOS-EU and FHWA TNM consider acceleration and deceleration. As traffic speed from traffic
forecast in Hong Kong is normally predicted for the entire road link instead of breaking down to muchfiner road segments, there are constraints to consider effect of acceleration and deceleration as
required in the CNOSSOS-EU and FHWA TNM.
According to CNOSSOS-EU, the effect of acceleration and deceleration is much more difficult tobe estimated for a traffic stream than individual vehicles as it depends on the behaviour of individual
drivers, location, time, traffic conditions, etc. As the traffic data in Hong Kong normally do notcontain speed data for different vehicle classes, the uncertainty on the estimation of acceleration anddeceleration of the traffic can be higher than the effect on noise emission. Therefore, CNOSSOS -EU
suggests the effect of acceleration and deceleration may be neglected in most situations. However,the EU Member States may wish to consider the effect of acceleration and deceleration for specificnoise mitigation measures, such as green waves or crossing transformations.
According to a research (12), experiment results in Dublin city showed that the actual effect ofacceleration and deceleration for crossings with traffic light and roundabout on road traffic noise
emission is lower than that predicted by CNOSSOS-EU. As such, the research recommended that itis acceptable not to apply correction for crossings with traffic light and roundabout. Therefore, theeffect of acceleration and deceleration of vehicles is not considered in current stage of RTNAM-HK.
However, the effect of acceleration and deceleration on noise emission can be reviewed in the future.
2.8 Air Temperature and Studded Tyres
Given the variation of the air temperature over the last decade in Hong Kong is small, the correction
is insignificant (<0.15dB) and hence not considered in RTNAM-HK. Studded tyres are also
uncommon in Hong Kong and it is appropriate to neglect the associated effect.

3. REVIEW OF NOISE PROPAGATION
3.1 Distance and Angle of Views
By dividing the traffic stream of a road section into very short segments and each represented bya point source, it would enhance more accurate prediction of the effects of reflections and diffractions
in situations such as short sections of road, short barriers/ enclosures with gaps between, multiple
reflections from elevated structures and portal opening, etc. and would in turn facilitate a better
optimisation of the noise mitigation measures. Thus, it is proposed to adopt point source propagationin RTNAM-HK. As such, the correction for angle of view is not applicable.
3.2 Ground Absorption and Atmospheric Absorption
While all the 3 methodologies provide methodologies to correct for ground absorption, the groundcharacteristics in urban area of Hong Kong are mainly paved as hard -surfaced area. Besides, mostof the buildings are medium to high density (i.e. 20 – 40 storeys) and the separation distance is from
10m to 100m. Thus, the ground absorption effects could be neglected.
There is no consideration of atmospheric absorption in CRTN, while in CNOSSOS -EU and FHWA
TNM, the attenuation due to atmospheric absorption is included. Indeed for short separation distanceas in most cases in Hong Kong, the effect would not be significant.
3.3 Reflection
In CRTN, noise reflection considers two types of reflection effect, including 1) Façade effect - acorrection of +2.5dB(A); 2) Reflection from opposite façade - a correction where there are houses,
other substantial buildings or a wall beyond the traffic stream along the opposite site of the road etc.
In CNOSSOS-EU, reflections on vertical obstacles, including buildings and barriers, are dealt withby means of image sources. The absorptive nature of the surface of the obstacle is also consideredwith the absorption coefficient. FHWA TNM assumes that reflections create extra propagation paths
resulting in increased noise level at NSR, which are dealt with by means of image sources as in
CNOSSOS-EU. Reflection surfaces are also assigned with respective absorption coefficients. BothCNOSSOS-EU and FHWA-TNM consider reflection based on a similar approach as in ISO 9613-2“Acoustics – Attenuation of sound during propagation outdoors – Part 2: General method of
calculation”.

Reflections within the compact nature of Hong Kong are obviously more complex than many other
cities. Sensitivity tests using Odeon model have been conducted to investigate the effect of multiple
reflection due to street canyon. The first case is in Mongkok where buildings of about 70m tall arelocated within 5m from both sides of narrow roads (see Figure 3). The second case is the new town
in Hang Hau with taller buildings (i.e. 100-120m tall) and wider roads (i.e. 40m).

Figure 3 – Oden analysis for Mong Kok and Hang Hau
Results for both cases reveal that the correction for reflection would reach its maximum when theorder of reflection reaches 5, although the increase would be less dominant from 3 rd order to 5 th order.
For narrow roads, the increase in noise levels at lower level would be higher than that at higher levels.
It is therefore recommended that a maximum of up to 5 th order is sufficient for most circumstances inHong Kong.
3.4 Diffraction
Diffraction in CRTN depends on the position of source, receiver and the point where the diffractingedge along the top of the obstacles cuts the vertical plane. The degree of diffraction is calculated
from the path difference of the diffracted ray and direct ray, and whether the receiver is within theilluminated or shadow zones.
In CNOSSOS-EU, diffraction is considered as frequency-dependent, which considers both the top
and lateral of each obstacle located on the propagation path. The degree of diffraction depends on
the path difference which is calculated in a vertical plane containing the source and NSR. Besides,
the attenuation through diffraction by the obstacle after reflection on a vertical obstacle is also
considered. According to the Amendments of CNOSSOS-EU:2015 recommended by the European
working group (13), lateral diffraction is considered only when the direct path is blocked (i.e. lateral
diffraction will be neglected at illuminated zone). The attenuation due to diffraction is considered inboth favourable and homogeneous conditions in CNOSSOS-EU.
In FHWA TNM, diffractions are used to simulate areas of propagation paths that pass through topedge of a barrier in the vertical geometry, and determines the decreased energy from the original pathbased on the angles and distances to the source and NSR from the diffraction point.
In Hong Kong, noise barriers are erected to abate excessive road traffic noise and some of thesenoise barriers may need to be shortened to allow for accessibility and sight-line for safety. Elevatedroad sections etc. in close proximity of receivers would also complicate the noise propagation schemes.
Besides, flyover structures in close proximity would also diffract noise to receivers (see Figure 4
below).

25m

Figure 4 – Examples of local challenges for diffraction

To better simulate the particular circumstances in Hong Kong, it is suggested to consider lateralpath and its diffraction according to the internationally accepted ISO 9613-2 methodology in RTNAMHK to avoid the road traffic noise impact to NSRs being potentially underestimated. In addition tobuildings, barriers and elevated structures, terrain may also contribute to the attenuation due todiffraction. It is therefore also recommended to consider diffraction by terrain in RTNAM-HK.

4. REVIEW OF INNOVATIVE NOISE MITIGATION MEASURES
Innovative Noise Mitigation Designs (INMD) have been widely adopted in Hong Kong to abateroad traffic noise, which include Acoustic Window (Baffle Type) (AW(BT)) and Enhanced Acoustic
Balcony (EAB) in Side-Hung type (EAB(ST)) and Baffle type (EAB(BT)). Both AW(BT) and EAB
are developed by the EPD in collaboration with Housing Department and Buildings Department,
Government of HKSAR and are designed to effectively reduce the noise while still meet with thenatural ventilation requirements for residential developments. INMDs are designed to reduce thenoise level affecting the noise sensitive rooms as compared with those standard openable windows.
These specially designed INMDs are unique to Hong Kong and none of the current internationallyestablished road traffic noise assessment methodologies would cater for this.
EPD has conducted extensive studies on the acoustic performance of different configurations of
INMD. For AW(BT) serving rooms of typical dimensions, the performance would be in the order of-6 dB(A) to -9 dB(A), while for EAB(BT), the performance would be much improved to -8 dB(A) to– 11 dB(A), both depending on the relative configuration of the INMD and the road. The addition ofsound absorptive material for both AW(BT) and EAB could further reduce the noise by 1.5 dB(A).
There is a need to customize the RTNAM-HK to incorporate INMD to better suit the Hong Kongsituations.

5. CONCLUSIONS
While the application of CRTN in Hong Kong has been found to be reliable for road traffic noiseassessment, the unique landform and spatial setting in Hong Kong, plus the development of electricand new energy vehicles, have driven the need to enhance the predicted methodology to better suitthe Hong Kong situations. A critical review has been conducted on three common international
assessment methodologies to identify the most suitable approaches that should be adopted and aspects
that need to be better modified in RTNAM-HK.
Aspect (Source Term)
Proposed Approach for RTNAM-HK
Vehicle classification• Adopt a more refined vehicle classification and allow for an
additional category for EVs
Source configuration• Adopt point sources
Source lanes• Adopt at middle of carriageway (typical cases) OR at middle of eachlane as needed
Rolling / propulsion noise• Conduct noise measurements to establish Hong Kong dataset
Road Surfacing• Adopt a 2.5dB(A) for PMFC and PMSMA6 respectively in both highspeed & local roads
Age of road surface• No need for correction
Gradient• Consider correction for both uphill & downhill traffic
Acceleration / deceleration • Possibly to be reviewed in next stage
Air temperature• No need for correction
Studded tyres• Not applicable in Hong Kong
Aspect (Propagation)
Distance
Angle of view
GroundAtmospheric absorption
Reflection
Diffraction

Proposed Approach for RTNAM-HK• Adopt point source propagation• Not applicable for point source propagation• Adopt hard ground propagation• May consider correction at 25 oC & relative humidity of 70%• Adopt 3 rd order for general cases & higher order for complex
conditions• Include both top & lateral paths for diffraction

GIS is the most commonly adopted platform for visualization and spatial analysis i n Hong Kong.
HKSAR has been releasing many city information that are GIS compatible (e .g. terrain, buildings,
roads etc.). It is proposed to implement the RTNAM-HK in a GIS platform with an Add-in to beestablished for installation.

6. Acknowledgement
Special acknowledgements to EPD for their initiation of this project and their efforts to conduct
noise measurements to establish noise source terms of vehicles in Hong Kong.

REFERENCES
1. EPD. Innovation Noise Mitigation Designs and Measures in Hong Kong [Available from:https://www.epd.gov.hk/epd/Innovative/greeny/eng/.
2. Hong Kong's Climate Action Plan 2050 [press release]. 2021.
3. EPD.
Promotion
ofElectric
Vehicles[Available
from:https://www.epd.gov.hk/epd/english/environmentinhk/air/promotion_ev/promotion_ev.html.
4. Chatterjee S. CS RSK. An Investigation of Lane Flow Distribution on Multi-Lane Highways under Mixed
Traffic Condition. TPMDC 2016, 2016.
5. Wu N. Equilibrium of Lane Flow Distribution on Motorways. Transportation Research Record Journal of
the Transportation Research Board. 2005.
6. Peeters B. BGV. Correcting the CNOSSOS-EU road noise emission values. Euronoise 2018, 2018. p.
1245-52.
7. Pallas M. A. BAL. Determination of a Noise Emission Model for French Medium-heavy Vehicles.
Internoise 2019; Madrid, Spain, 2019.
8. Leon G.D. et al., Implementation of CNOSSOS-EU Method for Road Noise in Italy. 23rd International
Congress on Acoustics; Aachen, Germany, 2019.
9. Chui E. LCK, Leung J., Yeung M. Applying Low Noise Road Surfacing to Reduce Road Traffic Noise in
Hong Kong. 22nd International Congress on Acoustics, 2016.
10. Sandberg U. The Global Experience in Usinh Low-noise Road Surfaces: A Benchmark Report. 2009.
11. Emanuel H. et al., Traffic Noise Emission Modelling at Lower Speeds. 23th International Conferenceof Sound And Vibration; Athens, Greece, 2016.
12. Murphy E. FJP, Rice H. J., Kennedy J. Transitioning to Strategic Noise Mapping under CNOSSOS-EU(Noise-Adapt). EPA Research Report; 2021.
13. Kok A. and Beek A.V. National Institute for Public Health and the Environment – Ministry of Health,Welfare and Sport, Amendments for CNOSSOS-EU. 2019.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022 in Gyeongju, Korea

ABS-0126

Assessing the environmental noise of next-generation regional jet
aircraft concepts in a generic airport scenario
Gil FELIX GRECO1; Felix WIENKE2; Lothar BERTSCH2; Tobias P. RING1; Sabine C. LANGER1
1
2

Institute for Acoustics (InA), Technische Universität Braunschweig, 38106 Braunschweig, Germany

Institute of Aerodynamics and Flow Technology, German Aerospace Center (DLR), 37073 Göttingen, Germany

ABSTRACT
Within the scope of the Coordinated Research Center (CRC) 880, concepts of regional aircraft were designed
to perform short takeoff and landing (STOL) operations with low fuel consumption and low noise emissions.
These characteristics were achieved through the use of active high-lift systems combined with propulsion
systems especially tailored for this purpose. In this contribution, the impact promoted by two different CRC
880 regional aircraft concepts equipped with ultra high-bypass ratio (UHBR) turbofan jet engines isinvestigated with respect to environmental noise. For this purpose, aircraft noise simulations are conductedusing the software sonAIR, where air traffic scenarios around a generic medium-size airport are investigated.
The integration of novel aircraft concepts into the scenarios is made possible by the DLR’s in-house tools
FlipNA and PANAM, which are capable of providing realistic predictions of the flight performance and noise
emissions of the CRC 880 aircraft during departure and approach procedures. In total, two air traffic scenarios
including the CRC 880 aircraft are evaluated with respect to a reference scenario based on the German
legislation defining noise protection zones around airports as assessment criteria.
Keywords: Environmental noise, Airport noise, Aircraft noise

1. INTRODUCTION
The fundamentals of active high-lift systems (HLS) were extensively investigated within the scopeof the CRC 880 [1]. The main motivation behind the development of active HLS lies in the fact thatsuch devices make aircraft capable of performing STOL operations, especially if combined with
propulsion systems specifically designed for this purpose. With the ability to operate on short runways,
regional airports can be integrated into flight connections thereby enhancing flight route networks.
Moreover, the aircraft designs developed within the CRC 880 have been conceptualized with a focus
on the reduction of fuel consumption and noise levels compared to current regional aircraft in order
to contribute to a sustainable growth of the air traffic.
The main goal of this study is to assess the impact of two next-generation regional jet aircraft
concepts developed within the CRC 880 with respect to environmental noise. The selected aircraft
incorporate some of the technologies investigated in the CRC 880 such as UHBR turbofan engines
and active HLS. Their individual potential for noise reduction was previously addressed in the works
of Blinstrub et al. [2–4]. This study aims to extend the previous investigations by analyzing the impactof the selected CRC 880 aircraft on environmental noise when introduced into a generic yet realistic
air traffic scenario, as experienced around a typical medium-size airport.
For this purpose, three air traffic scenarios are considered in this study: a reference scenario andtwo additional scenarios including the CRC 880 vehicles. The environmental noise is assessed basedon the German legislation defining noise protection zones around airports. The selected CRC 880
aircraft for this study are presented in Section 2 while the methodology used to perform the aircraft
noise simulations and the proposed air traffic scenarios are described in Section 3. Finally, the results
are presented in Section 4 while the conclusions of this study are provided in Section 5.

1
2

g.felix-greco@tu-braunschweig.de, t.ring@tu-braunschweig.de, s.langer@tu-braunschweig.de
Felix.Wienke@dlr.de, Lothar.Bertsch@dlr.de2. THE CRC 880 AIRCRAFT CONCEPTS
Several aircraft concepts were developed within the scope of the CRC 880 [1], including aircraftwith turbofan and turboprop propulsion systems. All aircraft concepts were designed using thePreliminary Aircraft Design and Optimization (PrADO) tool [5], with the following requirements:
2000 km range with maximum payload (100 PAX and 2.2 t freight), and a cruise Mach number of 0.78
for the jet aircraft. For this study, three vehicles were selected, namely the KON1, KON1v3, and the
REF3 aircraft (see Figure 1). Although a brief description of the aforementioned aircraft is provided
hereafter, the reader is referred to the work of Blinstrub et al. [4] for a comprehensive description.
The KON1 represents the technology in the year of 2010 in the class of 100-seaters and is adopted
in this study as the reference aircraft. It is equipped with a conventional HLS comprised of slats andFowler flaps, and two turbofan engines with a bypass ratio of 5. The KON1 aircraft has a required
takeoff roll distance of 1618 m, and its maximum takeoff mass (MTOM) and maximum landing weight(MLW) are 44736 kg and 42128 kg, respectively.
The KON1v3 is a variant of the KON1 aircraft, which is equipped with two UHBR geared turbofan
engines with a bypass ratio of 17. Thus, the KON1v3 represents the expected propulsion technology
for the year of 2030. Even though the larger UHBR engines of the KON1v3 introduces an increase ofmass with respect to the KON1, this is compensated by a 25% reduction of specific fuel consumption
and a 15% reduction of the required takeoff roll distance [4]. The KON1v3 aircraft has a MTOM and
MLW of 42705 kg and 40763 kg, respectively.
The REF3 aircraft is equipped with the same UHBR geared engines of the KON1v3, but has anengine over the wing (EOW) design thus avoiding jet-flap interaction effects and promoting theshielding of the fan noise by design. However, the EOW topology leads to an increased drag due tothe requirement of large pylon structures, which are necessary in order to sustain the engines and also
to accommodate the landing gears. The REF3 aircraft is equipped with an active HLS comprised ofcoanda flaps at the trailing edge and flexible droop noses at the leading edge. The active HLS noise
is described in an approximate manner due to the lack of appropriate and sufficient models [2–4]. The
effect of such an active HLS combined with the UHBR geared engines is a reduction of the required
takeoff roll distance by about 43% with respect to the KON1 aircraft [4]. The REF3 aircraft has a
MTOM and MLW of 46200 kg and 43921 kg, respectively.

(a) KON1v3.

(b) REF3.

Figure 1 – CRC 880 regional aircraft concepts considered in this study.

3. TOOLS AND METHODOLOGY
The present study employs the simulation process introduced by Delfs et al. [6] and Zellmann etal. [7] to include existing and novel aircraft types in environmental noise studies of large air traffic
scenarios. The simulation process used in this study is summarized in Figure 2.
In a general sense, the noise assessment framework is based on two main simulation tools: 1) the
DLR’s in-house Parametric Aircraft Noise Analysis Module (PANAM) [8], and 2) the aircraft noise
simulation software sonAIR [9]. Within this contribution, PANAM is used to predict the noise
emissions of novel aircraft concepts under operational conditions. This is done using a comprehensive
framework which includes: a) the aircraft design, using the PrADO tool [5]; and b) the calculation of
the engine noise shielding using the SHADOW tool [10]. Based on the inputs provided by the PrADO

and SHADOW tools, PANAM predicts the noise emission of the relevant noise sources onboard of an
operational aircraft, i.e. engine and airframe noise components, using a set of semi-empirical models[8]. Moreover, the operational conditions of the CRC 880 aircraft along prescribed flight trajectories
are calculated using the DLR’s FlipNA tool [3]. The noise emission predictions and flight profile
calculations of the CRC 880 aircraft considered in this study are based on this framework, which isaddressed in details by Ref. [2–4].
The noise emission predictions of the CRC 880 aircraft provided by PANAM are further processed
for use within the sonAIR software environment, where aircraft noise simulations of different air
traffic scenarios are conducted. The conversion procedure of aircraft noise emission models from
PANAM to sonAIR is addressed in details by Ref. [11]. The scenarios consider a generic yet realistic
airport configuration, where the air traffic is composed of a prescribed number of novel and existing
aircraft movements per flight route. In the following sections, more details are provided for each ofthese aspects.

Figure 2 – Air traffic scenario assessment: overall simulation process used in this study to
perform aircraft noise simulations including novel and existent aircraft types.
3.1 Airport setup
The airport configuration considered in this study is presented in Figure 3. It resembles a typicalmedium-size airport composed of two parallel runways positioned ~1.4 km apart. The longer runway(09L/27R) has a total usable length of ~3.2 km while the shorter runway (09R/27L) has a usable
length of ~2.3 km. Both runways have a width of 45.11 m. The terrain topography around the airport
is configured with a constant height of 50 m above mean sea level. The terrain cover is modelled as ahomogenous grass surface, with a flow resistivity of 200 kg s −1m−2.
Instead of considering individual flightpaths for each flight movement, t he air traffic is modelled
through a set of idealized flight routes, composed of four straight arrival routes (TA00 to TA03), four
straight departure routes (TD00 to TD03) and four curved departure routes (TD04 to TD07). In orderto model a realistic spatial dispersion of aircraft movements around a particular route, each approachand departure route is composed of a backbone track and four sub-tracks. For the creation and noise
simulation of the sub-tracks, sonAIR implements the procedure described by the ECAC Doc. 29 [12].

Figure 3 – Airport setup including the runway layout and the configuration of flight routes.
3.2 Number of movements and aircraft fleet mix
In order to replicate a typical medium-size airport, this study is based on data concerning the air
traffic at the Hannover Airport (IATA code: HAJ), Germany. First, an approximate total number ofmovements (commercial and non-commercial flights) for the six months of the year of 2019 with thelargest number of flight movements (from May to October) was obtained from Ref. [13]. The numberof movements was further divided into day- and night-time periods based on the air traffic statistics
provided by Ref. [14]. A representative aircraft fleet mix was determined from the database provided
by Olive et al. [15] by extracting the number of flight movements per aircraft type occurring at the
airport along this timeframe. Finally, the obtained aircraft fleet mix was adapted in order to include
the CRC 880 aircraft, which now represents all aircraft types with similar propulsion system, takeoff
weight, and max. number of passengers. The air traffic used in this study is presented in Figure 4.

(a)

(b)

Figure 4 – Air traffic data considered in this study: (a) total number of flight movements permonth and time-period of the day, and (b) percentage number of flight movements per aircraft typewith respect to the total number of flight movements.

3.3 Aircraft noise emission models and flight profiles
In order to describe all the aircraft types considered in this study (see Section 3.2), noise emission
models from three different origins are used. For existing aircraft types, noise emission models fromthe sonAIR [16,17] and the SANC-DB [18] databases are used. The reduced sonAIR emission models
used in this study do not account explicitly for the aerodynamic configuration of the aircraft while the
SANC-DB models describes the aircraft noise emissions based on the flight-phase. For the CRC 880
aircraft considered in this study (see Section 2), the noise emissions are predicted using the DLR’s
framework [2–4,8]. The aircraft types used in this study and their respective noise emission models
are summarized in Table 1.
Table 1 – Aircraft types and corresponding noise emission models used in this study.
#
1
2
3
4
5
6
7
8
9

Aircraft
A319
A320
A321
ATR42
B738
C550
KON1
KON1v3REF3EngineV2522-5A
CFM56-5A
V2530-5A
PW127
CFM56-7B
PW530Atf (BPR=5)
gtf (BPR=17)
gtf (BPR=17)

Emission model
A32X_V2500
A32X_CFM56-5A
A32X_V2500
AT43_SANCDB
B73X_CFM56-7B
C550_SANCDB
CRC_880_KON1
CRC_880_KON1v3
CRC_880_REF3

OriginsonAIRsonAIRsonAIR
SANC-DB
sonAIR
SANC-DB
PANAM
PANAM
PANAM

N1app [%] N1dep [%]
55.2
83.9
54.2
91.3
53
89
–
–
55.3
93.1
–
–
Calculated
for eachflight segment

In sonAIR, the flight profile provides information about the aircraft operational parameters along
the flight trajectory. The required parameters are: cumulative distance from the runway, altitude above
ground level (AGL), true airspeed, air density and Mach number at the flight altitude, and fanrotational speed N1. In addition, the aerodynamic configuration of the aircraft (i.e. flaps and landing
gear handle) is required for the CRC 880 aircraft because their noise emission models consider it
explicitly.
For existing aircraft, data concerning the altitude and velocity profiles was obtained from theAircraft Noise and Performance Database (ANP) [19]. The ANP database provides standard approachprofiles based on the MLW of each aircraft type, while different options are available for departure inorder to account for the variability of aircraft takeoff weights. In this study, departure flight profiles
following the ICAO-B procedure and considering the maximum takeoff weight of each aircraft typeare used. Mean N1 values, i.e. constant values over the entire flight path, were obtained from the workof Zellmann [20]. The mean N1 values used for each aircraft type during the approach, N1app , andthe departure, N1dep , procedures are provided in Table 1. The International Standard Atmosphere(ISA) [21] is used to model the atmospheric variables (air density and sound speed) as a function of
the aircraft’s altitude.
For the CRC 880 aircraft, the operational conditions and aerodynamic configuration of the aircraft
are modeled explicitly for each segment of the flightpath using the DLR’s FlipNA tool [3]. For thisstudy, the continuous descent approach procedure and the ICAO-B departure procedure are calculated
based on the full MTOM and MLW of each CRC 880 aircraft (see Section 2). The altitudes of theflight profiles used in this study are presented in Figure 5. It should be noted that the fully simulated,
hence artificial, CRC 880 vehicles show comprehensive flight characteristics. For example, thesimulated departure altitude profiles are in good accordance with the measured profiles of the existing
aircraft. Differences can be directly attributed to the aircraft’s individual flight performance aspredicted.

(a) Approach.

(b) Departure.

Figure 5 – Altitude flight profiles considered in this study for different flight procedures.
3.4 Aircraft noise simulation and air traffic scenarios
The aircraft noise simulations are conducted using the ArcGIS-implemented version of thesoftware sonAIR [9,17]. The sonAIR simulation framework is formulated in 1/3 octave bands andmodels the aircraft noise emissions and the sound propagation separately. The simulation procedurein sonAIR is briefly described hereafter.
First, the input data concerning the airport environment (runway layout, ground topography andland cover) is provided. The terrain and the land cover are included in the sonAIR simulations as a
Digital Terrain Model (DTM) and a land cover raster, respectively, with a spatial resolution of 25 m.
In this study, wind velocity is neglected and the atmospheric properties are kept constant with atemperature of 20 °C, an atmospheric pressure of 1 atm and a relative air humidity of 60%.
Then, flight trajectories are created for each aircraft type and flight procedure by merging the
ground tracks with the flight profiles. The ground track represents the flight trajectory as a series ofpoints projected in the horizontal ground plane. The flight profiles contains the information about theaircraft operational conditions along the ground track. Later on, sound attenuations are computed
using the propagation model sonX [9] and each combination of aircraft type and route are simulated.
The noise contours are computed for each flight on a grid of receiver positions with a spatial
resolution of 150 m at a height of 4 m above the ground. Based on the energetic superposition of thenoise contours obtained for each aircraft movement along a flight route, cumulative noise metrics arecomputed in order to quantify the total noise from multiple aircraft movements occurring along agiven time period.
Based on the air traffic defined in Section 3.2, three scenarios are investigated in this study:

reference scenario: KON1 aircraft;scenario 1: KON1v3 aircraft; andscenario 2: REF3 aircraft.

Therefore, with the proposed scenarios, we focus on evaluating the impact of the CRC 880 aircraftin terms of environmental airport noise. For this purpose, it is assumed that the same aircraft fleet (seeFigure 4b) operates during the day- and night-time periods. Moreover, the number of movements per
aircraft type is equally distributed among the eight departure (TDXX) and four arrival (TAXX) routes
previously defined in Section 3.1. This assumption is the reason why the total number of movements
adopted in our study slightly differs from the official numbers provided by Ref. [13]. The number ofaircraft movements per aircraft type and flight route used for each scenario is presented in Table 2.

Table 2 – Number of flight movements per aircraft type, flight route, and time period of the day
used in each air traffic scenario investigated in this study.
Ref. scenario#

Aircraft

1

A319

2

A320

3

A321

4

ATR42

5

B738

6

C550

7

KON1

8

KON1v3

9

REF3
Total

Scenario 1

Scenario 2

TAXX

TDXX

TAXX

TDXX

TAXX

TDXX

339
108
847
270
424
135
424
135
593
189
339
108
1271
405
–
–
–
–
16948
5400

170
54
424
135
212
68
212
68
297
95
170
54
636
203
–
–
–
–
16968
5416

339
108
847
270
424
135
424
135
593
189
339
108
–
–
1271
405
–
–
16948
5400

170
54
424
135
212
68
212
68
297
95
170
54
–
–
636
203
–
–
16968
5416

339
108
847
270
424
135
424
135
593
189
339
108
–
–
–
–
1271
405
16948
5400

170
54
424
135
212
68
212
68
297
95
170
54
–
–
–
–
636
203
16968
5416

Total perscenario
2716
864
6780
2160
3392
1084
3392
1084
4748
1516
2716
864
10172
3244
10172
3244
10172
3244
33916
10816

Day
Night
Day
Night
Day
Night
Day
Night
Day
Night
Day
Night
Day
Night
Day
Night
Day
Night
Day
Night

3.5 Environmental noise assessment criteria
The German “Act for protection against aircraft noise” [22] is used as the basis for the noiseassessment in this study. This document establishes the criteria for the definition of noise protection
zones in the surroundings of airports. For existing commercial, civil airports with more than 25000
movements per year, the protection zones encompass the residential areas in which the A-weighted
equivalent sound pressure level, 𝐿A,eq, caused by aircraft operations exceeds the following values:

day-time protection zone 1: 𝐿A,eq,day = 65 dBA;
day-time protection zone 2: 𝐿A,eq,day = 60 dBA; andnight-time protection zone: 𝐿A,eq,night = 55 dBA.

The day-time interval ranges from 06 AM and 10 PM while the night-time interval starts at 10 PM
and ends at 06 AM. The assessment period is defined as the six months of the forecast year with thelargest number of flight movements.

4. RESULTS
Figure 6 presents a comparison between the results obtained for the reference scenario and scenario
1. In the reference scenario, the day protection zone 1 envelops only the runways and the airport
premises and does not reach any residential area, as shown in Figure 6a. However, the day protection
zone 2 extends about 500 m and 1 km from the larger and smaller runways’ thresholds, respectively,
thus affecting residents living in the vicinities of the airport. In addition, the night protection zone islarger and potentially affects residents living as far as 2.5 km and 3 km from the larger and smaller
runways’ thresholds.
It is evident in Figure 6a and Figure 6b, that the noise reductions achieved by the KON1v3 aircraft
are mainly relevant for the night protection zone, whose isocontour area is reduced by ~10.6%. Since
the only difference between the reference scenario and the scenario 1 is the change from the KON1 to
the KON1v3 aircraft, it can be speculated that the introduction of regional aircraft concepts withUHBR engines alone would achieve a small effect on the air traffic noise. This statement mainly holds
for an air traffic scenario with a similar share of aircraft types as the one adopted in this study.

However, it could be expected that an increase of the number of regional aircraft flight movementswith respect to larger medium-range aircraft would lead to a further area reduction of the noise
protection zones.

(a)(b)
Figure 6 – Comparison of noise protection zones between the reference scenario (solid lines) andthe scenario 1 (dashed lines): (a) absolute noise contour levels and (b) isocontour area reduction.
A comparison between the results obtained for the reference scenario and the scenario 2 ispresented in Figure 7. It is interesting to note that the combination of the EOW design and STOL
capabilities of the REF3 aircraft, which requires ~43% less takeoff roll distance than the KON1
aircraft, leads to an isocontour area reduction of ~39.5% for the day protection zone 1. Even though
this protection zone does not affect any residential areas in this study, this finding is relevant because
it indicates that such an aircraft concept would potentially provide a substantial reduction of criticalnoise levels at areas near the airport premises. This argument is further supported by the fact that theisocontour area of the day protection zone 2, which is within the near range of the airport, is reduced
further by ~11.9% compared to scenario 1. In addition, the isocontour area reduction obtained in the
scenario 2 for the night protection zone is verified to be ~6.4% higher than for scenario 1, which
could also be attributed to the STOL capabilities of the REF3 aircraft. Table 3 summarizes the results
obtained in this study for all scenarios investigated.

(a)(b)
Figure 7 – Comparison of noise protection zones between the reference scenario (solid lines) andthe scenario 2 (dashed lines): (a) absolute noise contour levels and (b) isocontour area reduction.

Table 3 – Summary of results obtained for the different air traffic scenarios in terms of absolute
isocontour area size and relative percentage change with respect to the reference scenario.
Noise protection zone

Ref. scenario

Scenario 1% change

Scenario 2% change

Day 1 [km2]

1.35

1.27

-6.13

0.82

-39.53

Day 2 [km2]

5.31

5.00

-5.78

4.37

-17.73Night [km2]

13.6

12.6

-10.5711.28-17.05

Finally, we investigate to which extent the isocontour area reductions achieved by the CRC 880
aircraft would hold for an increasing air traffic. For this purpose, we gradually increase the total
number of flight movements while keeping all the other air traffic parameters (see Section 3.2) andassumptions (see Section 3.4) unchanged. Figure 8 presents the results obtained for the isocontour
area reductions of the different noise protection zones and scenarios as a function of the air traffic
growth. The relative percentage isocontour area reductions are computed with respect to the baseline
reference air traffic scenario.

(a) Scenario 1.
(b) Scenario 2.
Figure 8 – Relative isocontour area reduction with respect to the reference scenario as a functionof the air traffic growth. Negative reduction values implies an increase of the isocontour areas.
The results reported in Figure 8 shows a clear trend towards the increase of the isocontour areas
as the air traffic increases for both scenarios and all noise protection zones. Moreover, a similar decayrate of the day 2 and night protection zones’ isocontour area reduction as a function of the air traffic
growth is observed for both scenario 1 and scenario 2 while a steeper decay rate is observed for the
day 1 protection zone. Concerning the scenario 1, Figure 8a shows that the amount of air traffic growthnecessary to cancel out the reduction of the noise protection zones promoted by the KON1v3 aircraft
depends on the noise protection zone, being ~2.7% for the day 1 zone, ~5.4% for the day 2 zone
and ~10.6% for the night protection zone. This indicates that the introduction of regional aircraftwith UHBR engines for flight movements during the night-time would be beneficial if the air traffic
in a certain airport is expected to increase in the future. However, the variation of the isocontour area
reduction threshold is smaller between the different noise protection zones for the scenario 2 (seeFigure 8b), ranging from ~16% (night protection zone) to ~18% (day 2 protection zone). Thissuggests that the introduction of the REF3 aircraft would be beneficial for reducing the area size ofall protection zones when the air traffic in a certain airport with a similar share of aircraft types.

5. CONCLUSIONSIn this study, the impact of two CRC 880 concepts of regional jet aircraft is investigated withrespect to environmental airport noise. For this purpose, a comprehensive simulation process is used,which enables the assessment of air traffic scenarios around a generic medium-size airport including
novel and existing aircraft types. Moreover, the environmental airport noise is assessed based on thedefinition of noise protection zones around airports, as defined by the German federal government.
In general, the results showed the potential of the CRC 880 aircraft for mitigating the air traffic
noise by promoting the reduction of the noise protection zones’ area. Thus, the CRC 880 aircraft are
expected to potentially contribute to decrease the exposure of citizens living in residential areas near
airfields to critical noise levels. In particular, both CRC 880 aircraft presented relevant potential for
reducing the area of the noise protection zone established for the night -time period. Moreover, it was
shown that this finding holds also for an increasing air traffic growth.
Furthermore, this study demonstrated that the REF3 aircraft has a more consistent and larger
potential for reducing the area of the noise protection zones than the KON1v3 aircraft. This is likely
due to the fan noise shielding promoted by the EOW design, and the STOL capabilities of the REF3
aircraft, which is able to takeoff at a considerably reduced runway roll distance than the KON1v3
aircraft. Thus, by flying higher at distances closer to the airport, the REF3 benefits areas near the
airport premises. Ultimately, it has to be mentioned that the CRC 880 vehicles have not been optimized
towards low-noise characteristics. Further adaptation of the vehicle designs and low -noise
performance would result in significantly larger overall noise reductions.

ACKNOWLEDGEMENTS
We would like to acknowledge the funding provided by the Deutsche Forschungsgemeinschaft(DFG, German Research Foundation) under Germany’s Excellence Strategy – EXC 2163/1 –Sustainable and Energy Efficient Aviation – Project-ID 390881007. We would like to thank Dr.
Christoph Zellmann and Dr. Beat Schäffer from Empa for the technical support concerning the sonAIR
emission models and the PANAM to sonAIR data conversion procedure. Furthermore, the internal
review and valuable feedback from Dr. Rainer Schmid (DLR) are highly appreciated.

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7. Zellmann C, Bertsch L, Schwab O, Wolters F, Delfs J. Aircraft noise assessment of next-generation narrowbody aircraft. In Proceedings of Internoise 2019. The 48th International Congress and Exhibition on NoiseControl Engineering, Institute for Noise Control Engineering, 2019.

8. Bertsch L. Noise Prediction within Conceptual Aircraft Design. Doctoral Thesis, Technische Universität
Braunschweig, 2013. https://doi.org/10.34912/n0is3-d3sign
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Simulation Tool for Single Flight Prediction and Noise Mapping. Acta Acustica united with Acustica.
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24.03.2022

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022 in Gyeongju, Korea

ABS-0150

Investigation of Noise Sources and Radiated Sound Power of Civil
Transport Aircraft
Yahui SONG1; Hong HOU2; Shu YUE3; Yuanming ZHAO4; Gaoyu FAN5
1
2

Institute of Aircraft Flight Test Technology, Chinese Flight Test Establishment, Xi’an 710089, China

Key Laboratory of Ocean Acoustics and Sensing, Ministry of Industry and Information Technology, School ofMarine Science and Technology, Northwestern Polytechnical University, 710072 Xi’an, China

3

Key Laboratory of Ocean Acoustics and Sensing, Ministry of Industry and Information Technology, School ofMarine Science and Technology, Northwestern Polytechnical University, 710072 Xi’an, China
4

Institute of Aircraft Flight Test Technology, Chinese Flight Test Establishment, Xi’an 710089, China

5

Institute of Aircraft Flight Test Technology, Chinese Flight Test Establishment, Xi’an 710089, China

ABSTRACT
In order to evaluate a large civil transport category aircraft noise accurately and scientifically, a series of field
experiments have been carried out to identify the main noise sources and estimate their radiated sound power.
A126-element logarithmic spiral microphone array is optimized to improve the sound source recognition
performance. The Doppler effect of the received signal of microphone array elements is eliminated byinterpolating the de-dopplerisation. The main airframe and engine noise sources are identified bybeamforming algorithm, and the radiated sound power is estimated by sound intensity scaling method. The
spectral characteristics analysis and quantitative separation of the main noise sources of the aircraft are
implemented. The sound power estimation results of the identified engine noise source are in good agreementwith the working state parameters of the engines, which verifies that the sound intensity scaling method isreliable. The flight test results show that the designed microphone array and the beamforming algorithm iswell suited to assess main noise sources of the civil aircraft flyover status. These noise sources identification
results provide valuable information for the further noise reduction design of the aircraft.
Keywords: Beamforming, Noise Source Identification, Sound Power Estimation

1. INTRODUCTION
In noise source identification, in addition to obtaining the azimuth information of the noise source,
it is also necessary to estimate the radiation strength of the noise source. By quantitatively analyzing
the strength of the noise source, the contribution of its radiation can be sorted, which is helpful tocarry out targeted vibration and noise reduction work. However, in the current flight test, most of theresearches are focused on the identification of the main noise sources of the aircraft, and most of theresearches on the quantification of the intensity of the moving sound source are focused on thequantitative reconstruction of the sound pressure spectrum of the measured sound source to evaluate
the noise strength, without involving the sound power measurement. Only sound pressure level cannot
fully describe the radiation strength characteristics of the noise source.
The objects of traditional sound power test methods and related test standards are mainly staticsound sources. For a moving sound source, due to the existence of the Doppler effect, the sound signal
1
2

songyahuilym@163.com
houhong@nwpu.edu.cn

318792795604@mail.nwpu.edu.cn

collected by the microphone is distorted, and the phenomenon of frequency shift and frequency band
expansion occurs, which makes it difficult to measure the sound power of the moving sound source.
Up to now, there is no relevant ISO test standard, and there are few radiated sound power measurement
methods that can be used for moving sound sources. Especially for a sound source with a relatively
large volume and a fast moving velocity such as an aircraft, it is difficult to obtain the radiated soundpower of the main noise source in the actual navigation process, and the existing research work stilllacks effective test method.
In order to solve the above problems, a 126-element multi-arm logarithmic spiral microphone arrayis designed and the Doppler effect in the received signal of the array is eliminated by nonlinear
interpolation. The conversion relationship between the output of the microphone array beam and theradiated sound power of the sound source is used to estimate the radiated sound power of the mainsound source of the aircraft based on the identification of the noise source. The flight test experimental
data results of airplane C919 show that the sound power estimation results of the identified engine
noise sources are in good agreement with the engine operating state parameters, which verifies theeffectiveness of the method.

2. CONVERSION MODEL BETWEEN SPIRAL ARRAY BEAM OUTPUT AND
SOUND POWER OF THE NOISE SOURCEAs shown in Figure 1, a spiral microphone array is used for noise source identification. The
coordinate system is established with the center of the array as the origin. Assuming that the reference
sound pressure at the origin is P e, the angular frequency, wavelength and sound velocity of the incident
sound source are ω, λ and c, respectively, r m is the position vector of the m-th microphone, k is thewavenumber vector in the focusing direction, and k0 is the wave number vector in the incident
direction of the source, k=ω/c is the wave number, and the test distance L is large enough to ensurethat the sound wave received by each microphone is a plane wave. Defined W ( K ) as the array mode,
it is used to express the beam output of the array at each search point. Where K is the difference
between the wavenumber vector in the focusing direction and the wavenumber vector in the incident
direction of the sound source, then the output of the conventional beam in the focusing direction k
can be expressed as:
22(1)
B 2 ( k ,  )  Pe W ( K )1 M -1 jK rm
1 M -1 j ( k - k0 )rm
e
=
(2)

e
M m=0
M m=0
For uniform multi-arm logarithmic spiral microphone array, its array mode can be approximately
expressed as:
J ( KD / 2)W ( Κ )  W (K )  2 1(3)KD / 2
Where, K is the module of the projection of K on the xoy plane, and the first null of the first-order
Bessel function:(4)
1  K1 D / 2  1.22
Projection of array beam output on xoy plane:B  k,   Pe W  k sin  (5)
W (K ) 

Where  is the included angle between focusing direction and microphone array axis. The beam
output at each grid point in the main lobe region of the spatial spectrum is described by the grid point's
distance from the axis and its azimuth. When the main lobe region is narrow, approximate substitution
can be used:(6)
R  L tan   L sin 
The beam output result of the main lobe region can be approximately expressed as: kR 
B  R,    P0 W   , R  R1
(7)
 L 
R 1 is the radius of the main lobe region, that is, the position of the first null of the array beam
output:
K
2 L
L
(8)
R1  L sin 1  L 1  1  1.22
k

Dk

D

The scaling factor  is used to transform the beam output to make the integral result of the scaledbeam output in the main lobe region equal to the radiated hemispherical sound power of the soundsource.
2
R1 2
R1
2  kR 
2
2
2 Pe

B
(
R
,

)
RdRd


2

P
W
RdR

2

L
(9)
e 
 
0 0
c
 L 
0
The scaling factor  can be expressed as
L2



R1

(10)

kR
 c  W  RdR
L 

0
2

Assume u  ( kR L )  D 2  , the integral in equation (4.10) can be expressed as:
R1

2



2 

2

1
1
 kR 
 J 1 (u )   2 L 
 2 L   J 1 (u ) 
W
RdR

2
udu







0  L 0  u   kD 
 2 u udu
 kD  0 2

1

 J 1 (u ) 

(11)
(12)

 2 u udu  0.419
0The scaling factor can be approximately expressed as:
2
5.88  D  c   

(13)

z
R



SourceKθoFigure 1 – Noise source identification model of spiral microphone array

3. SOUND POWER ESTIMATION FOR MOVING SOUND SOURCES
There is relative motion between the noise source and the microphone array, resulting in Doppler
shift and amplitude gain of the signal received by the microphones. In order to accurately identify thenoise source and estimate its radiation strength, Doppler effects need to be eliminated. The signal
received by the microphone is sampled uniformly at the receiving time tr by the acquisition system.
y
x

0

10m

Figure 2– Schematic diagram of surface meshing of an aircraft
In order to eliminate the Doppler effect, the reference plane where the wing-body center plane islocated is meshed, and each grid point is approximately regarded as a point sound source. The distancevariation between each grid point and each element is different in the flight of passenger aircraft, so
it is necessary to perform the time-domain solution Doppler calculation on each grid point respectively.
The aircraft is in the flyover status and moves along the coordinate axis. Assume that the coordinate of agrid point of the aircraft are (xs+vt, ys, zs) and the coordinates of the m-th microphone array element is (xm,ym, zm). Assuming that the distance between the sound source position and each microphone array element is
Rm (te ) ,which can be expressed as follows:R(te )2  ( xm  ( xs  vte ))2  ( ym  ys ) 2  ( zm  zs )2
After eliminating the Doppler effect, the radiated signal of the sound source can be expressed:x (te )  xr (te  R(te ) / c ) R (te )

(14)
(15)

Assume te is the moment when the flight noise of the aircraft is generated at the grid, xr  tr  is the received
signal of the current microphone array element at the moment tr. The conversion relationship between the tr
and te is:tr  te  R ( te ) / c(16)
tr is the time when the noise radiated at the moment reaches the current microphone array element. The
time tr is a known sampling time. Due to the movement of the aircraft, the data collected at the time tr is thesound emitted at the time te. To establish the relationship between radiation time and collected data, theradiation time te of the sound source should be calculated first. Assume r 2  y 2  z 2 , M  v / c , equation.14
and 16 yield:
R ( te ) M ( xm  x s  vt r )  ( xm  x s  vtr ) 2  (1  M 2 ) r 2
1 M 2(17)

Since tr is uniform sampling, the corresponding radiation time series te is non-uniformity. It is necessary
to obtain uniformly sampled noise data by nonlinear interpolation. Finally, the static sound source signal
received by the microphone array is simulated. Therefore, the signal received by the microphone array froma moving sound source can be converted to stationary sound source signal by eliminating Doppler effect. On
this basis, the sound power estimation result of the moving sound source is obtained according to theconversion relationship between the beam output and the sound power.

4. EXPERIMENTAL PROCEDURE
4.1 Flight Test Introduction
A series of flight tests of a civil jet airplane C919 are conducted for studying the sound power
estimation method. This airplane is a twin-turbofan-engine trunk airliner, and the maximum take-off
mass is 78900kg. In order to identify the main noise of the main noise sources and estimate theirradiated sound power accurately, the airplane C919 flyover the microphone array in a straight line ata constant ground speed of 80m/s, with the target altitude of among 50m to 60m, as shown in Figure3(a) and Figure 3(b). The microphone array used in flight tests is a 9*14 multi-arm logarithmic spiralarray, arranged on the runway of Neifu Airport in China, as shown in Figure 3(c) and Figure 3(d).

(a) Flight test schemeb) The airplane C919 flyover the microphone array

(a) Spiral microphone array(b) Microphone array element
Figure 3– Flight test scheme of airplane C919
Before flight test, the phase and amplitude of each microphone were calibrated to ensure theaccuracy of sound location and sound power estimation results. Each microphone was fixed on analuminum plate with a length and width of 60 cm to eliminate the influence of reflected sound waves
from the ground. The flight path of the aircraft was recorded by D-GPS, and the time series of therecorded flight path and the time series of the sound acquisition system are unified into the same timeaxis.
4.2 Data Processing
Each data acquisition of flight run lasted about 40 seconds. All channels are sampled
simultaneously at 65536 Hz. The beam map is generated in a rectangular, 36m long and 22m wide
(22m by 36m) flat grid covering the main wing-body of the aircraft C919 with a space step size of
0.5m. Conduct de-dopplerization processing on all grid points, and use beamforming to obtain thenoise source identification map. Figure 4 shows the typical frequency spectrum of a flight test run
after de-dopplerization.

Figure 4– A typical frequency spectrum of aircraft C919 after de-dopplerization
It can be seen that the aircraft noise is mainly concentrated in the frequency range of 200Hz
to800Hz. The noise source is identified for each sub-band from 200Hz to 800Hz by beamforming.
The noise source identification map of the civil aircraft flyover status in each analysis frequency bandare given belowas shown in Figure 5. The radiated noise of major part of the aircraft is the broadband
noise source.

y
x

y
x

0

10m

(a) [200, 250] Hz

010m(b) [250, 315] Hz
y
x

y
x

0

10m

(c) [315, 400] Hz

010m(d) [400, 500] Hz
y
x

0

10m

y
x

0

10m

(c) [500, 630] Hz(d) [630, 800] Hz
Figure 5– Beamforming maps of aircraft noise sources
When the aircraft is in a straight line flight status with a constant speed, the nose landing gearand rear landing gear of the aircraft are in a retracted state, and the flaps, slats and other lifting
devices are closed. At this time, the main noise comes from engine noise. The aircraft is driven
by two turbofan engines that maintain the same working state during the flight test run. The left
and right engine turbofan speed percentages are 65.81% and 65.55%, respectively. Therefore, thenoise generated by these two engines should appear in the same frequency band. Reflected in the
beamforming noise source identification results, there should be two bright spots with the same
strength in the corresponding beam map. From the identification results as shown in Figure 5 ofaircraft noise sources in the above frequency bands, in each analysis frequency band, thestrongest noise sources mainly appear at the grid points near the engine, and there are two soundsources with comparable strength. On the basis of noise source identification results by
beamforming, the sound power estimation model in Section 2 is used to estimate the radiatedsound power of the noise source. The sound power estimation results under different analysisfrequency bands are recorded in Table 1.
Table 1 – Sound power estimation results in different frequency bands of airplane C919
Frequency, Hz

Left engine, dB

Right engine, dB

200-250

139.0

140.3

250-315

146.5

144.8

315-400

147.2

147.9

400-500

154.7

153.6

500-630

148.1

148.7

630-800

146.2

146.9

total

157.0

156.6

The frequency band in which the maximum results occur coincides with the frequency band of the
maximum amplitude after de-dopplerization. In the whole analysis frequency band, for the same subband, the sound power estimation results of the left and right engines are basically the same. This isbecause in the aircraft state parameters, the turbofan speed percentages of the two engines arebasically around 65%. The total sound power estimate for the left engine is 0.4dB higher than theright, which is also reasonable because the left engine turbofan revs (65.8%) slightly higher than theright engine (65.5%). Overall, the sound power estimation results are consistent with the left and rightengine working state parameters. It shows that the above noise source identification results andradiation strength estimation results are reliable.

5. CONCLUSION
In order to solve the problem of estimating the radiated sound power of the main noise source of
the aircraft during flight, a motion sound power estimation method is proposed. Firstly, the conversion
model of the beam output of the helical array and the radiated sound power of the sound source isdeduced, and the Doppler effect is eliminated by means of nonlinear interpolation, and the received
signal of the microphone array of the moving sound source is converted into the received signal of
the static sound source. The noise source is identified by beamforming and the sound power estimation
result is obtained according to the method in this paper. The noise source identification results andthe radiated sound power estimation results of flyover flight tests of airplane C919 are consistent withthe working state parameters of the engine. The research in this paper provides an effective solution
for the sound power measurement of aircraft noise.

ACKNOWLEDGEMENTS
The authors wish to thank Zhou Zhanting, Zhang Honglin, Wang Dongsen, Zhu Jianghui, Lu Xiaodong,
Tian Baoquan of Chinese Flight Test Establishment for their capable management of the project; Fan
Zhengbing, Zhang Xiaoliang of Chinese Flight Test Establishment for their helpful technical advice on
spiral array design; and to Li Xiaolu, Song Guozhi, Zhang Yuelin, Li Xinxia for their continued
advocacy work. The authors thank Fan Gaoyu, Gao wentao, Qin hao, Kou baozhi, Liu Likun of Chinese
Flight Test Establishment for their helpful work on flight tests. The authors gratefully acknowledge thenoise measurement devices operation assistance of Lu Di, Wang Bin, Zhang qun of BSWA Technology
Co, Ltd. Finally, the authors wish to express special thanks to the graduate student team of professor
Hou hong and Yang Jianhua of Northwestern Polytechnical University for their support of the flighttests.

REFERENCES[1] Khorrami M R, Lockard D P, Humphreys W M. Flight-Test Evaluation of Airframe Noise
Mitigation Technologies. AIAA/CEAS Aeroacoustics Conference in Atlanta. Georgia. June 2018.
[2] Lockard D P, Bestul K A. The Impact of Local Meteorological Conditions on Airframe Noise
Flight Test Data. AIAA/CEAS Aeroacoustics Conference in Atlanta, Georgia, June 2018.
[3] Humphreys W M, Lockard D P, Khorrami M R, McSwain R. Evaluation of Methods for InSitu Calibration of Field-Deployable Microphone Phased Arrays. AIAA Paper 2017-4176, June2017.
[4] Hald J. Combined NAH and Beamforming Using the Same Microphone Array. Sound andVibration,2004, 38, 18-27.
[5] Hald J. Estimation of Partial Area Sound Power Data with Beamforming. INTER-NOISE,
2005.
[6] J Panda, RG Seasholtz, KA Elam. Further Progress in Noise Source Identification in High
Speed Jets via Causality Principle. AIAA/CEAS Aeroacoustics Conference in Rome, Italy, June
2003.
[7] ] Yardibi T, Bahr C, Zawodny N, et al. Uncertainty Analysis of the Standard Delay-and-Sum
Beamformer and Array Calibration. AIAA/CEAS Aeroacoustics Conference in Miami, Florida,
June 2009.
[8] K Ehrenfried, L Koop. A Comparison of Iterative Deconvolution Algorithms for The Mappingof Acoustic Sources. AIAA/CEAS Aeroacoustics Conference in Cambridge, USA, June 2006.
[9] P Bohning, H A Siller. Study of a De-Convolution Method for Aircraft Flyover Measurements.
AIAA/CEAS Aeroacoustics Conference in Rome, Italy, June 2007.
[10] T Yardibi, J Li, P Stoica, et al. Sparsity Constrained Deconvolution Approaches for Acoustic
Source Mapping. AIAA/CEAS Aeroacoustics Conference in Vancouver, British Columbia
Canada, May 2008.

ABS-0487

Algorithm for Calculation of the Measured Single Fly-over Aircraft Noise
Thapana BOONCHOO(1) , Krittika LERTSAWAT(2)(1) Faculty of Science and Technology, Thammasat University, THAILAND, E-mail: thapanab@tu.ac.th(2) ENLAWTHAI Foundation, THAILAND, E-mail: krittikanonoise@gmail.com

ABSTRACTThe noise measurement data of the single fly-over aircraft noise levels are fundamental noise events information
to understand the existing situation of noise from aircraft operations under the major flight path near the airport.
The standardized noise measurement method is described under the ISO 20906 as a background study of Thai
version guidelines of the aircraft noise measurement in community. Under the major flight path, the noise
situation at the receiver sites in communities may need to be collected to contribute those noise data to the social
survey and noise-dose relationship study, and/or the noise impact assessment study, and other purposes. In thispaper, we will provide the post-processing algorithm which employs two-step clustering approach to calculate
the single events of fly-over aircraft noise in sound exposure levels from the measured time series of the 100
milliseconds A-weighted sound pressure levels by sound level meter complied with ISO/IEC 61672 class 1. It
will be useful for post-assessing the cumulative aircraft noise events in other sound descriptors as stated in ISO
1996 for environmental noise purposes.

Keywords: aircraft noise events, post-processing algorithm, python programming

1

INTRODUCTION

Noise measurement data of the single fly-over aircraft noise heard on the ground are important noise levels
to illustrate the existing human perception of aircraft noise at receiver location. Under the standardized noise
measurement methods, ISO 20906 and ICAO Annex 16 Volume I Part 3[4], the 500 milliseconds (ms) in Sound
Pressure Levels (L p in dB) with time duration are indicated to be measured and recorded into the sound level
meter (SLM) to automatically calculate in terms of the Sound Exposure Levels (LE,T in dB) of each fly-overaircraft noise event. [5] Not all SLM can give the results of the 500 ms measured data and calculation. Some ofthem in the commercial market will provide the automatically measured results of the aircraft noise calculation
by additional calculation modules that the users need to pay more money for.
In order to facilitate the noise calculation, we propose an algorithm that can automatically calculate thesingle fly-over aircraft noise of at least the 500 ms or shorter period of time duration of the measured datafrom the SLM using Python program. The user can use our algorithm and program to apply for the measured
noise in time series of the 100 ms measured noise events, recorded by SLM at the receiving point/measurement
position near the airport in the specific periods of time. Those measured noise data are in compliance with thestandardized measurement methods for aircraft noise measurement heard on the ground.
The single fly-over aircraft noise in terms of the sound exposure levels in decibels (dB) or decibels-A(dBA) will be fundamental noise data of single fly-over aircraft noise to determine many time-based noise descriptors/indicators. They will be applying to illustrate the aircraft noise impacts for the study of environmental
pollution issues or the aircraft/airport noise impact assessment. For examples we can include various noise
descriptors, calculating the noise impacts of aircraft operations; Equivalent Continuous Sound Pressure Levels(Leq,T in dB or dBA)(4), Day-Night Sound Levels (Ldn in dB or dBA)(6), Day-Evening-Night Sound Levels(Lden in dB or dBA)(7).

2

CONCEPTUAL DESIGN AND METHODOLOGY

2.1 Conceptual design
The calculation of single fly-over aircraft noise events is the main purpose of the conceptual design in this study.
The data processing workflow is designed to serve the main purpose, illustrated in Figure 1. The continuous
measurement data collection in time series noise levels is collected into the data storage of SLM. They aresources of aircraft noise events that go through the designed algorithms for event detection and classification.
The detected events are determined in combination with non-acoustical factors to identify the single fly-overaircraft noise events.

Continuous
Measurement

SLM atMeasurement
Positions

𝐿𝑝 (𝑡)

Sound event

Event
Detection

Probable AircraftSound Event

EventClassification

Event detection

Filtering (verification)

Preprocessing

Outlier Detection using
IQR method(Standardization +Smoothing)

K-Means
Clustering

Event
Identification

Identified AircraftSound Event

Actual Flight Logs

STEP 4

STEP 1

STEP 2

Peak Extraction(DBSCAN Clustering)
STEP 3

Proposed Algorithm

Figure 1. Data processing workflow
1

2.2 Dataset and description
The measured aircraft fly-over noise levels were collected at different measurement positions under the major
flight path around the Bangkok International Airport (BIA) from June to August of the year. The LAeq,100ms
of fly-over aircraft noise were continuously measured in SLOW time-weighting using the Rion NL-31 class I(ISO/IEC 61672) for plotting time-series graphs of the 7 days of noise events under the major take-off andlanding flight paths at different microphone heights (6–10 meters) above the ground with the conditions in ISO
20906:2009 and the Thai guideline on unattended aircraft noise monitoring method in the community[6].
The RION NL-31 class 1 was set to measure every 100 ms of the A-weighted sound pressure levels ofnoise events in the SLOW time-weighting network with a date and time stamp in text files. We can use
the original noise event files downloaded from SLM as the sample set of the measured noise events in timeseries for determining the single fly-over aircraft noise. The sample of noise data from all 7 measurement
positions was collected by SLM written into SD cards and downloaded into sequential text files for aircraft
noise determination by the Python program.
During the fieldwork study, we worked under the funding of the ENLAWTHAI foundation (NGOs) and the
AERC, Japan (NGOs). It was a limitation of flight log data usage in our research work. We tried to send theformal letter to ask permission to use actual flight log data from AEROTHAI, but we received a phone rejection.
We also tried to send a formal letter to the NBTC to ask for permission to use the radar receiver equipment,
but we were unable to get it because we are not a government agency. In this case, if the researcher was
not allowed to use the data, we could not formally complete these calculations, either. In Thailand, it shouldbe improved to either provide the actual data for all research purposes or allow the use of the receiver radarequipment to collect the data for research purposes in the future to support the research opportunities for Thai
researchers to get the actual data for accurate data validation on research work.
In order to protect the privacy of the original data of the actual flight log, we were not allowed to use
it during our fieldwork study. The actual flight log data was contributed by the government research groupthat was allowed to use it formally. Then, we will blind the heading of some specific data and the sourceof measurement noise data. We used the actual measured noise data from the field research data around the
Bangkok International Airport in Thailand in the past. Despite this, we will provide the actual noise levels, time
duration, date, time, aircraft type, measurement positions, model of SLM, and setting information in our datareport that will be enough to use in calculation and testing activities on data validation and verification. The
contributed actual flight logs were applied to match the actual flights with the fly-over aircraft noise calculation
by a Python program using the proposed algorithms.

Figure 2. Noise Measurement Positions around BIA
Sources: Krittika Lertsawat, 2015
All the noise data in the time series of each measurement position was input to the events detection algorithm to classify and calculate each fly-over single event noise [5] in the sound exposure level in decibels (LE,Tor SEL in dB) as shown in equation (1).
LE,T = 10log[ET ]dB re 20µ Pa
[E0 ]

(1)

where the sound exposure, ET and the reference value, E0 : 4x10−10 Pa2 .s
2.3 Algorithm and coding
In this section, we give the details of our proposed algorithms. The proposed algorithm consists of four main
steps, as shown in Figure 1. In what follows, we will describe the details of each step.
STEP 1 Preprocessing: The first step is smoothing out the given measured time-series (sound pressurelevel over time) using the well-known moving average method so that local variations in time-series can beattenuated, and then performing standardization so that data will have zero mean and unit variance.
STEP 2 K-means for peak sound level identification: In the second step, we use K-means [3] clusteringon the previous step’s time-series data to divide the values of the time-series into K groups; each group willcontain measurements with similar sound levels. In particular, K-means is a clustering algorithm which partition
data objects into K groups and it requires the preliminary knowledge about the number of groups of data as theparameter accepted by the algorithm. Based on our assumption that the noise of fly-over aircraft will possibly
have high amplitude (outstanding values), we could set the parameter K into a small number and then wechoose the first few clusters that contain large cluster representatives as they could be the peaks of the fly-overaircraft noises which can be extracted by the next step.
STEP 3 DBSCAN clustering for noise interval extraction: Once we obtain the clusters representing thepeaks of the fly-over aircraft noises, we proceed to step 3 to automatically extract such peaks from the wholetime-series. To this end, we employ another kind of clustering algorithm, which is DBSCAN [2], to groupthe data in the clusters from the previous step. Unlike K-Means, DBSCAN is another clustering algorithm thatwill group data into an arbitrary number of groups based on the density of data. Thus, it does not require thenumber of clusters for the algorithm. However, the algorithm instead accepts two parameters, which define thedegree of density for each cluster and the minimum distance among objects in each cluster. It is important tonote that our proposed algorithm will perform DBSCAN on the "time" dimension but not the time-series data
itself, since the sound interval of a single fly-over aircraft corresponds to consecutive outstanding values fromthe previous step. As described earlier, DBSCAN does not require the number of clusters as its parameter, thus
it can automatically find the groups or clusters of consecutive outstanding values from the time-series without
knowing the number of the fly-over aircraft ahead of time. The clusters in each cluster discovered by the
DBSCAN algorithm can be recognized as peaks found by our algorithm.
STEP 4 Filtering : Finally, the last step is to filter out the peak, which should not be classified as thenoise of fly-over aircraft, by examining the duration of the extracted peak and the other noise-domain-relatedcharacteristics of the peak. Later, we provide the discussion about the results after this filter step in the next
section.

3

EXPERIMENTAL RESULTS

3.1 Single events of fly-over aircraft noise
The proposed algorithms are coded by the Python programming language to make an aircraft noise calculation.
A noise measurement time series of L pA,100ms fed into the program to detect and identify a single fly-overaircraft noise event. The time duration of each fly-over aircraft noise event shall be exceeded by 12 seconds
to be confirmably detected as a fly-over aircraft noise event. The sound exposure levels in dBA of each single
fly-over aircraft noise event were calculated by the proposed algorithm using the Python programming language
as shown in the equation (2) or (3). The results are tabulated into a data sheet format for next step calculation.
LE = 10log[

R t2 2
t1 p (t)dt

E0

] dB

(2)

T
] dB
T0

(3)

or
LE = L p,T + 10log[

where the reference time duration T0 was set to 1 second.
Under the detection and identification process, the acoustic factors are applied to the algorithms to computethe single event noise. There is a time duration of noise events and their noise levels in dBA. The time seriesof all single noise events are plotted to see the overview of the output results from the Python program in each
step, as shown in Figure 3. Figure 3 (a) shows the original time-series of a noise measurement time series
example over 5 hours (starting from 20XX-03-12 19.00 at the measurement position N1). After the time-series
has been smoothed out by the moving average and standardization methods, the time-series will be attenuated
so that noise is diminished, allowing patterns to stand out more clearly, as shown in the example in Figure 3

(b). Comparing Figure 3 (a) and Figure 3 (b), we can see that the noisy data will be flattened and the peaks
can be more clearly seen. From this step, we employed the K-Means clustering algorithm with K set to 2
on the smoothed data; thus, the algorithm produced only two clusters, which are shown in Figure 3 (c) and
Figure 3 (d). More specifically, Figure 3 (c) depicts the cluster 1, whose cluster representative is smaller thanthe cluster 2 that is shown in Figure 3 (d). We ignored cluster 1 and considered cluster 2 as peaks that are
possibly aircraft noise events. In this example, the algorithm can detect 18 peaks, which are indicated by thegreen lines in Figure 3 (e).

(a) Original Data
80
60
0

25000

50000

75000

100000125000150000175000125000150000175000125000150000175000125000150000175000150000175000(b) Smoothed Data using Moving Average
50
0

0

25000

50000

75000

100000(c) Cluster 1
50
25
0

0

25000

50000

75000

100000(d) Cluster 2 (Maximum Cluster)
75
70
65
0

25000

50000

75000

100000(e) Detected Peaks on the Maximum Cluster(s)
50
0

0

25000

50000

75000

100000125000Figure 3. Results from the steps 2, 3, 4 of the two-step clustering algorithm described in the section 2.3
In addition, we considered the non-acoustical factors in the identification process of the program to confirm
the noise events of aircraft fly-over. Non-acoustical factors include the geographical location of each noise
measurement point; specific information on arrival or departure flights; background noise levels from otheractivities; and other observations.
3.2 Data validation
After all the peaks that have been detected from the previous steps, it requires a validation process on thedetected peaks such that we only include the noise events of fly-over aircraft in the final result set. Here, weconsider two characteristics of the detected peaks, which are time duration and the sound pressure level ofthe events. Figure 4 and Figure 5 show the histograms of the time duration and the sound pressure level ofthe events that the proposed algorithm can detect over 7 days at the 7 measurement positions. We decided tofilter out those events whose characteristics are recognized as outliers. To this end, we utilized the well-known
statistical IQR method [7] to identify the outliers. The boundaries (upper and lower bounds) of the time durationand sound pressure level are illustrated in the red lines in Figure 4 and Figure 5, respectively. The detectedpeaks that are recognized as outliers (reside outside the boundaries) will be discarded from the final result set.
For example, the outlier peaks, which are outside the upper and lower bounds of time duration, may turn out tobe the peaks of non-fly-over aircraft but somewhat producing noise longer and shorter than the fly-over aircraft(e.g. trucks or train cars passing by the measurement station, reproductive sources from daily life of people inthe vicinity of microphone positions).
N1

N2

N3

0.08

0.02

0.02

0.03
0.02

0.00

50
100 150 200
Time duration (seconds)

0

N5

Density

0.01
Mean: 24.34
050
100 150 200
Time duration (seconds)

0.00

50
100 150 200
Time duration (seconds)

0.035

0.012

0.030

0.010

0.025

0.008
0.006

Mean: 43.04

0

Mean: 56.38

0.002

0.005
50
100 150 200
Time duration (seconds)

0.0000050
100 150 200
Time duration (seconds)

0.015
0.010

0

50
100 150 200
Time duration (seconds)

0.0025

0.020

0.004

0.000

0.0075N7

0.014

Density

0.04

0.02

Mean: 29.72

N6

0.05

0.03

0.01

Mean: 28.55

Mean: 24.70
00.01000.0050

0.01

0.01

0.03

Density

0.04

DensityDensityDensity

0.01250.03

0.05

Density

0.0150

0.04

0.06

0.00

0.0175

0.04

0.07

0.00

N4

0.05

0.000

Mean: 28.53
050
100 150 200
Time duration (seconds)

Figure 4. Sample histogram results of Time duration. The red dotted lines indicate outlier boundary.
The detected peak obtained from the Python program at this stage, inside of the outliers, is somewhat
detected fly-over aircraft noise. The assumptions on selected outliers in this stage refer to both time duration
and aircraft noise levels being measurable in time series of every 100 milliseconds by SLM at the measurement
positions and -15 dB, respectively, as an acoustic factor. It is our task to make sure that all detected peaks are
the fly-over aircraft noise in the next steps in data validation, considering other acoustic factors and non-acoustic
factors.

4

DISCUSSION

4.1 Observation and limitation of calculation program
All calculated noise events of fly-over aircraft in each noise measurement position were identified and confirmedin terms of LAE in dBA for each aircraft noise. The acoustical factors, the time duration of noise events, and

N1

N2

N3

0.05

0.07
0.06

0.040

0.02

0.02

Mean: 86.19
0.00

50
100 150 200
Sound Exposure Level (dBA)

0.025

0.025

0.020

0.020

0

N5

50
100 150 200
Sound Exposure Level (dBA)50
100 150 200
Sound Exposure Level (dBA)

Mean: 99.32

0.005
0.000

0

50
100 150 200
Sound Exposure Level (dBA)

N7

0.035
0.04

0.030
0.03

0.025

0.02

0.03
DensityDensityDensity

0

N6

0.04

0.020
0.015
0.010

0.01
Mean: 100.07
0.00

Mean: 87.03

0.005
0.000

0.015
0.010

0.010

Mean: 87.86
0

0.030

0.015

0.01

0.01
0.00

0.03

DensityDensityDensity

0.05

0.03

0.030

0.035

0.04

0.04

N4
0.035

Density

0.08

0

50
100 150 200
Sound Exposure Level (dBA)

Mean: 105.18

0.005
0.000

0

50
100 150 200
Sound Exposure Level (dBA)

0.02
0.01
Mean: 104.28
0.00

0

50
100 150 200
Sound Exposure Level (dBA)

Figure 5. Sample histogram results of calculated single fly-over aircraft noise events. The red dotted lines
indicate outlier boundary.
their noise levels are prioritized to be key factors in identifying aircraft noise in the Python program. While the
non-acoustical factors were obtained from the specific actual flight logs and other observations, the geographicallocation of the measurement microphone of SLM, specific information on arrival or departure flight, and runway
usage are the second steps to confirm all aircraft noise events.
The noise measurement location plays a key role in the measured data in time-series noise levels. Eachfly-over aircraft flies by the measurement equipment at a different displacement in the same measurement position, resulting in the different sound exposure levels heard on the ground. The background noise levels of
its environmental noise are influenced by local activities and are a normal situation in the daily life of people
living in the community. The higher background noise levels that concurrently occurred at the same period of
the fly-over aircraft may reduce the effectiveness of the proposed algorithm to detect and identify aircraft noise
events in the Python post-processing program.
Some of the measurement positions were on the roof-top of a house in a dense community. The loud noise
might occur during some of their activities. While some others were on the roof-top of a low-rise residential andoffice building, the noise exposure levels may have included the noise from the air-conditioning and ventilation
ventilation. However, all measurement positions in this field study have met the site selection criteria of ISO
20906 [5] and Thai guidelines [6]. All of the measurement positions were located in the community area that
may be affected by aircraft noise from BIA. It was the main purpose of the noise measurement study, indeed.
These acoustical factors may cause a deviation in noise exposure level data of some measurement positionin the detection and identification of fly-over aircraft noise in post-processing noise data, depending on the noisefrom local activities. We decided to propose many steps of detection and identification algorithms to solve theproblems. It is clearly exposed to loud noise when the -15 dB between sound pressure levels from the noise
source and from background noise in the dB addition equation. [1]When the results were determined under the detection and identification process, the non-acoustical factors
from the actual flight logs and other observations played an important role in illustrating the situation; i.e., geographical location, the coverage area from the threshold of runway usage, the specific information on arrival or
departure flights, aircraft types, meteorological data, etc. This data may be used to select alternative algorithms
and set up conditions in Python programming.
The results obtained from the outliers algorithm are compared with non-acoustic data, particularly from the
air traffic volume reported in the actual flight logs, in order to confirm that they are the aircraft fly-over noise
events, recorded by SLM at the measurement position.
Comparing the results of Python calculations with the flight log data makes it difficult to match individual
fly-over noise events data to individual aircraft in the flight log table because aircraft at runway and SLM positions have different lagging time stamps on both their departure and arrival flights. A lag time influences many
dependent non-acoustic factors, including aircraft speed, maximum take-off weight, wind speed, and grounddistance between the runway and measurement position. Nevertheless, we can try to estimate it roughly for
determining the acceptable lag time in each measurement position to validate aircraft noise events in the Python
calculation. A lag time influences the effectiveness of the identification process and needs improvement in futurestudies.
In general, airport operators, air traffic controllers, and civil aviation regulators report air traffic volume datain aviation statistics records. In practice, the scheduled flight table will be provided to all parties to confirm theflight schedules at an airport prior to the actual flight operations occurring for their specific purposes. Whenthe actual flight operations have already taken place. Air traffic controller (ATC) at that airport will report theactual flight operations or so called "actual flight logs" that happened in the past in their aviation statistics. Thisactual flight logs in at the same time as the noise measurement periods used in our study.
The stored information sent from the aircraft radar of each flight in the ATC database includes the individual
actual flight path operations of aircraft during departure and arrival operations at the airport. All actual aircraft
operations are stored in a database over a period of time to provide actual flight performance statistics. In
addition, they will be used in the review of fee-based or tax information from airlines and in investigations ofpotential safety and accidents that may have occurred. The actual flight operations path logs from radar are
better than the information in the flight log table for model validation in our case study. However, we were notauthorized by authority to use such information or install any radar receiver devices during our fieldwork study.
Although there are currently private companies that provide these data services, such as Flightradar24, which
provides online data services with a paid subscription. Furthermore, we are unsure about the availability ofprevious data in the same time period as our fieldwork study. We were also unable to find funding to support
our research at this time.
4.2 Application of experimental results
The results of a single fly-over aircraft sound exposure level heard on the ground will be applied to many
noise indicators in an environmental noise study field, such as the equivalent continuous sound pressure levels
in decibels (Leq,T in dB)(4), the 24-hour equivalent continuous sound pressure levels in decibels-A (Leq,24hr
in dBA)(5), the day-night sound levels in decibels-A (Ldn in dBA)(6), the day-evening-night sound levels indecibels-A (Lden in dBA)(7), the night sound levels in decibels-A (Lnight in dBA)(8), other indicators.
Equivalent Continuous Sound Pressure Levels in decibels (Leq,T in dB):
Leq,T =

1 R t2 2
T t1 p (t)dt] dB re 20µ Pa
10log[
p20(4)24-hour Equivalent Continuous Sound Pressure Levels in decibels-A (LAeq,24hr in dBA):
LAeq,24hr = 10log[

Li
1 24∑ 10 10 ] dBA
24 i=1

Day-Night Sound Levels in decibels-A (Ldn in dBA):(5)

Ldn = 10log[

9
Ld (07:00−22:00)
Ln +10(22:00−07:00)
1 15
10
10
+ ∑ 10)] dBA( ∑ 1024 d=1n=1(6)

Day-Evening-Night Sound Levels in decibels-A (Lden in dBA):
Lden = 10log[

39
Ld (07:00−19:00)
Le +5(19:00−22:00)
Ln +10(22:00−07:00)
1 12
10
10
10
( ∑ 10+ ∑ 10+ ∑ 10)] dBA
24 d=1
e=1
n=1

(7)

Night Noise Levels in decibels-A (Lnight in dBA):n

Lnight = 10log ∑ 10

LAE,i (22:00−06:00)
10− 10logT dBA

(8)

i=1

where the sound exposure levels of events(i) (LAE,i in dBA) and time duration (T) in of events noise occur
in seconds.
The calculated aircraft noise exposure levels from all measurement positions using the proposed algorithm inPython programming are shown in Table 1. Then, the two examples of calculated equivalent continuous soundpressure levels in terms of Leq.24hr , Ldn , and Lden from a departure receptor position (N1) and an arrival position(N3) are shown in Figure 6.
Table 1. The calculated aircraft noise exposure levels in different indicators
Leq,24hr

Ldn

Lden

dBA

dBA

dBA

Departure flights

93-114100-118

110-119

Arrival flights

82-10987-117

89-117

These indicators are beneficial for many studies in the fields of airport noise management, airport noise
impact assessment, noise-dose relationship study, etc. Although the recommended noise indicators for environmental noise impacts on health and well-being in the European Union are Lden and Lnight , only the LAeq,24hr
is used in Thai environmental regulations against noise impacts, with the exception of aircraft noise heard on
the ground, which is recommended Ldn . In this study, the proposed algorithm automatically calculated the measured noise data of aircraft fly-over noise events to LAE of a single fly-over noise event, calculating to LAeq,1hr ,LAeq,24hr , Ldn , and Lden to be beneficial in the noise-dose relationship study and public disclosure noise exposurelevel around BIA, also.

5

RECOMMENDATION AND FUTURE STUDY

Aircraft noise measurement is a key activity in the study of the existing noise situation in the vicinity of
airports. The proposed algorithms in our Python program can facilitate the aircraft noise calculation from noise
measurement data under the criteria of ISO 20906 and the Thai guidelines on measurement method for aircraft
noise exposure levels in the community reasonably. Not only for the data disclosure purpose of measured
noise data but also for the research study on airport noise management and the dose-relationship study of noise
annoyance around airports, which both gain benefit from it. Those studies are based on the important data setof noise situations around airports to illustrate the adverse impacts of noise on human health and well-being.
The proposed algorithm in the Python program will conveniently facilitate the noise calculation in those studies
as well.
For the future work of the algorithm development, it is interesting to employ more advanced machine learning techniques (e.g., deep learning) and also include non-acoustic features to detect and identify outlier eventsin the filtering (verification) step, such that the identification of single fly-over aircraft noise events can be moreN1: ARIVAL

91.0
85.9

86.4

91.4

93.2

92.1

91.8
86.1

87.3

86.6

82.8

85.5

93.4

Lden

94.6

93.4

94.3

Ldn

91.4

87.8

89.7

91.0

93.1

Leq,24hr

N1: DAY 1

N1: DAY 2

N1: DAY 3

N1: DAY 4

N1: DAY 5

N1: DAY 6

N1: DAY 7

N3: DEPARTURE

105.1

105.0

105.1

104.8

107.7

107.6

97.2

98.1

100.1

100.1

104.7

98.9

104.5

105.3

105.2

95.3

98.4

103.2

103.1

Lden

107.8

Ldn
107.6

Leq,24hr

N3: DAY 1

N3: DAY 2

N3: DAY 3

N3: DAY 4

N3: DAY 5

N3: DAY 6

N3: DAY 7

Figure 6. The calculated aircraft noise exposure levels in different indicators (bar chart)
1
accurate. In addition, we also plan to extend our algorithm such that it is scalable to larger datasets. For more
testing experiments of the proposed algorithms and Python program, we also plan to collect aircraft noise eventsfrom other airports with different brands of standardized SLM, asking for support to get the radar data of actual
flight path operations.
Depending on our study, some observations and limitations can be drawn from the new set of assumptions
for applying the proposed algorithm and python program to calculate the single fly-over aircraft noise eventsby general SLM under ISO/IEC 61672 class 1 in the future study. Those assumptions include the site selection
criteria for the noise measurement position underneath the major flight path of a specific airport, the coverage distance from the runway threshold, background noise level, and time duration of fly-over aircraft. Thoselimitations affect the results’ accuracy and precision.

ACKNOWLEDGEMENT
We would like to thank the government research group, ENLAWTHAI Foundation research project, and AERC
for all contributed data using in our work. We also would like to thank Associate Professor Dr. Supet Jirakajohnkool for making GIS-based noise measurement positions.

REFERENCES[1] Bruel and Kjaer. Environmental Noise, <https://www.bksv.com/media/doc/br1626.pdf> accessed 11th July 2022.
Brüel Kjær Sound Vibration Measurement A/S, 2001.
[2] M. Ester, H.-P. Kriegel, J. Sander, and X. Xu. A density-based algorithm for discovering clusters in large
spatial databases with noise. In Proceedings of the Second International Conference on Knowledge Discovery
and Data Mining, KDD’96, page 226–231. AAAI Press, 1996.
[3] J. A. Hartigan and M. A. Wong. Algorithm as 136: A k-means clustering algorithm. Journal of the Royal
Statistical Society. Series C (Applied Statistics), 28(1):100–108, 1979.
[4] ICAO. Annex 16 - environmental protection - volume i - aircraft noise, 7th edition, july 2014. 2014.
[5] ISO. ISO 20906:2009 Acoustics – Unattended monitoring of aircraft sound in the vicinity of airports. International Organization for Standardization, 2009.
[6] PCD.
Guidelineonunattended
aircraftnoise
monitoringincommunity,<https://www.pcd.go.th/wp-content/uploads/2020/05/pcdnew-2020-05-180 8 − 18 − 375 11049.pd f>accessed11thJuly2022. PollutionControlDepartmento f T hailand, 2013.
[7] C. M. Salgado, C. Azevedo, H. Proença, and S. M. Vieira. Noise Versus Outliers, pages 163–183. Springer
International Publishing, Cham, 2016.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022 in Gyeongju, Korea

ABS-0011

Flyover simulations for a multicopter air-taxi with rotor speed
variations
Michael W. BAUER1
1

Munich Aeroacoustics UG, Germany

ABSTRACT
There are already numerous studies regarding noise, related to Urban Air Mobility (UAM), focusing mainlyon the noise emission of individual air-taxis, but also to some extent on community noise, which will becaused by UAM air traffic. One peculiar aspect of some air-taxis will be their control by changing therotational speed (rpm) of the rotors or propellers. In consequence, noise footprints may be stronger dependingon the vehicle’s temporary operational status than for common aircraft or helicopters. This study provides astatistical analysis of major noise metrics in flyover situations with changing rpm of a generic air-taxi system.
The aircraft was equipped with electrical propulsion consisting of 15 propellers. Starting from a nominal rpm,
which was assumed for a static flyover – as for example during a typical situation for noise certification –the rotational speed of the propellers was allowed to vary in a certain range. Because there are no detailed
operational conditions available for any air-taxi system, the rpm distribution was assumed to follow a
Gaussian profile. This assumption yielded acoustic results, with overall flyover numbers of 5,558 for the airtaxi in the simulations, which are going to be discussed.
Keywords: air-taxi, UAM community noise

1. INTRODUCTION
Very first UAM noise simulations showed, that UAM noise can be cre ated in regions, where
currently no significant aircraft noise is occurring [1]. Besides this, air-taxi operations and the related
noise must be investigated especially in the vicinity of vertiports, because here the noise situation willstrongly depend on the flight attitudes of the specific aircraft. Because of the variety of different airtaxi concepts, their expected individual noise characteristics [2] will depend on the air-taxi’s specificoperational conditions during approach or take-off.
Some of the expected future air-transport systems are designed as multicopters, each with a numberof rotors or propellers above the passenger cabin. The aircraft is then stab ilised and controlled byrotating those rotors with different rotational speed rpm (revolutions per minute). Since this canhappen constantly in certain flight conditions (e.g., when approaching a vertiport) the permanentchange of rpm will result in changes of emitted sound power and the position of the propeller tonesin the noise spectrum.
This study is trying to understand how changes of propeller rpm in an air-taxi’s operation affects
noise spectra and noise levels on the ground.

2. AIR-TAXI MODEL
2.1 Propeller RPM Variations
The simulations are using a realistic multicoper-type air-taxi [3] with 15 propellers, installed above
the passengers’ cabin. The main technical data of this aircraft are shown in Table 1. To simulate therpm variation, a Gaussian distribution between ± 38.6 % around the nominal rpm has been assumed.
This can be understood that a certain rpm, during a representative period of operational time, is applied
to the rotors accordingly. The reason are missing manufacturer data how the rpm control will be
implemented into a larger air-taxi, which may differ from the control of smaller drones used, e.g., as1michael.bauer@muc-aero.com

toys and for parcel delivery. The Gaussian distribution has been chosen since it may not be realisticthat the aircraft is constantly flying with rpm at its limits, so a certain “clustering” around the nominal
value appears to represent the situation sufficiently for this study. Figure 1 shows the used air-taxi’s
rpm.
Table 1 – Air-taxi technical data
UAM Classification

multicopter

max. take-off mass [kg]

820

wings

w/o

propulsion

electric

propulsion typerotorsnumber of rotors/propellers

15

propeller/rotor mounting

above cabinrequired power [kW]90number of blades2

blade area [m2]0.0737

rotor/propeller diameter [m]2.2

nominal rpm [1/min]

777

number of passengers

2

Figure 1 – Gaussian rpm distribution. rpm = 0 represents the nominal rpm of 777 min-1.
An additional simplification was made assumeing that all rotors of the multicopter are at same rpm
during the later flyover. In reality the rpm settings for each propeller may be different which could
also be implemented as a certain distribution.
99 air-taxis were created for the simulations, each representing a specific rpm in the r equired range.
To achieve the Gaussian relation between the different rpm, taking into account that for a flyover atlast one aircraft has to be simulated, the distribution function was realized by different aircraft
quantities, i.e., 1 aircraft was flying with the lowest/highest rpm, 135 air-taxis were operated atnominal rpm. In total, 5,558 aircraft were simulated to be flying in this test case.

2.2 Air-Taxi Noise Emission
The change of rpm was understood just for the use for stabilising and/or controlling the attitude of
the air-taxi, not for acceleration means. Therefore, the noise emission spectra are changing more orless every moment. This spectral variation is shown in Figure 2, providing the sound power spectra
for a propeller with nominal rpm and the upper/lower limits. There is clear change in the tonal part
whereas the broadband part does not change too much since it is more or less rpm independent [4].

Figure 2 – Sound power spectra for minimum, nominal, and maximum rpm.
Air-Taxi Flyover Computation
The change or adaptation of the rotational speed for the rotors was understood just for the use ofstabilising and/or controlling the attitude of the air-taxi, not for acceleration means. Therefor the
flyover velocity has been kept constant at 30 m/s throughout the simulations. The air-taxi was assigned
to an altitude of 400 m above the ground. 5 microphone positions were simulated to achieve different
angles of observation, because the noise on the ground strongly depends on the taxi’s sources’
directivities. One observer has been selected directly under the flight path (flyover point), the others
were in lateral distances, shown in Table 2 together with the resultin g observation angles.
2.3

Table 2 – Microphone positions and observation angles
Lateral distance, mAngle, °

0

0.0

200

14.1

400

26.7

600

45.1

800

63.5

3. SIMULATION RESULTS
To develop a first understanding how different rpm of a full-scale air-taxi will be influencing thenoise on the ground, SEL (sound exposure level) and EPNL (equivalent perceived noise level) wereselected as the main metrics for these single events. Despite more than 5,000 air-taxi movements have

been simulated (as explained above this was just to establish the Gaussian rpm distribution), eachaircraft type’s results were treated as a single-event. Figures 3 to 6 show the behavior of SEL and
EPNL at the 5 observer points with different rotor speeds. The diagrams provide the values, changing
with rpm, and their averages.

Figure 3 – SEL and EPNL for the flyover point with varying rpm.

Figure 4 – SEL and EPNL for a lateral distance of 200 m to flyover point with varying rpm.

Figure 5 – SEL and EPNL for a lateral distance of 400 m to flyover point with varying rpm.

Figure 6 – SEL and EPNL for a lateral distance of 600 m to flyover point with varying rpm.

Figure 7 – SEL and EPNL for a lateral distance of 800 m to flyover point with varying rpm.

Figure 8 – Pseudo-time-averaged spectra at flyover point (OBS-0) and the 4 lateral positions.

For developing a better understanding on the perceived spectra at the observer positions, a pseudo time-averaging was introduced to reduce the information from the more than 5,000 spectra at eachobserver: all spectra were weighted with the Gaussian distribution shown in Figure 1 and averaged
over the total number of spectra. The result should represent one single spectrum, similar to a time
average for rpm(t). Figure 8 provides the 5 spectra, averaged as described.
While averaging “smears out” noise characteristics, especially of the varying tonal components,
the instantaneous spectra should represent the listener’s personal impression of the flyover noise muchbetter. Figures 9 to 13 show the maximum SPL 3 rd -octave band spectra for each observer. The tonal
part appears to be stronger compared to the flyover point directly below the flight path, at even larger
distances. This is related to the directivity of the multicopter’s rotor noise. The (pseudo) time averaged
spectra show higher tonality for the observers with higher observation angles, too.

Figure 9 – Maximum sound pressure spectra at flyover point for increasing rpm. The lowest shown 3rdOctave band frequency is 25 Hz.

Figure 10 – Maximum sound pressure spectra at 200 m lateral distance to flyover point.

Figure 11 – Maximum sound pressure spectra at 400 m lateral distance to flyover point.

Figure 12 – Maximum sound pressure spectra at 600 m lateral distance to flyover point.

Figure 13 – Maximum sound pressure spectra at 800 m lateral distance to flyover point.

4. CONCLUSIONSThe standard metrics SEL and EPNL at the different observer positions show a logarithmic
dependency on the rpm. This is not surprising because the rpm is one main parameter that determines
the noise generation. But the propeller tones in the lower frequency range, which mainly create therelative difference between SEL an EPNL, are strongly influenced by the observation angles. This is
the reason why SEL > EPNL for the centerline, but changing to EPNL > SEL for observers fartheraway.
The influence of the directivity of the propeller tones, which seems to be characteristic for the
specific installation situation, becomes very clear when looking at the instantaneous, maximum
spectra of Figures 9 to 13, where the increase of the tonal part with increasing lateral distance isevident, while the overall sound pressure level is decreasing due to the larger distance. The shape ofthe broadband part is merely affected (see Figure 8). But purely averaged spectra will suffer loss of
information and may not represent an appropriate instrument for air-taxi noise assessment in thevicinity of a vertiport.
As a consequence, the changes of rpm – especially fast changes when flying the air-taxi in acontrolled operating way into a vertiport – can strongly affect the communities at higher distance tothe vertiport and lead to higher annoyance there. Of course, the noise from an air-taxi with larger andslower rotors is very different from the noise, e.g., from a small toy qua dcopter, which generates
higher frequencies and thus may be more annoying.
In the principle, due to lower frequencies and slower changes of the rpm, larger, passenger carrying
air-taxis may be less annoying while using rpm control for stabilised flight operations. But this still
depends on the air-taxis individual propulsion installation, its operational modes, and its related
aeroacoustic properties (e.g., spatial noise radiation).

REFERENCES
1. Bauer, M., First Assessment of Community Noise for a Simulated Scenario of New Urban Air Traffic,
26th International Congress on Sound and Vibration (ICSV), Montréal, 2019.
2. Bauer M., “Air-Taxis: An outline of peculiarities from future air-traffic noise” (Presentation), German
Aerospace Congress, 31 August - 2 September 2021, Bremen, Germany.
3. Bauer M., Redmann, D., “ATEFA – Project’s results on UAM air traffic noise and air-taxi certification”,
INTER.NOISE 2022, 21-24 August 2022, Glasgow, Scotland.

ABS-0237

Speech enhancement using ego-noise references with a microphone arrayembedded in an unmanned aerial vehicle
Elisa TENGAN(1) , Thomas DIETZEN(1) , Santiago RUIZ(1) , Mansour ALKMIM(2,3) , João CARDENUTO(2,3) , Toon VAN
WATERSCHOOT(1)(1) Dept. of Electrical Engineering (ESAT-STADIUS), KU Leuven, Leuven, Belgium, elisa.tengan@esat.kuleuven.be(2) Siemens Digital Industries Software, Leuven, Belgium(3) Dept. of Mechanical Engineering, KU Leuven, Leuven, Belgium

ABSTRACTA method is proposed for performing speech enhancement using ego-noise references with a microphone arrayembedded in an unmanned aerial vehicle (UAV). The ego-noise reference signals are captured with microphones
located near the UAV’s propellers and used in the prior knowledge multichannel Wiener filter (PK-MWF) toobtain the speech correlation matrix estimate. Speech presence probability (SPP) can be estimated for detecting
speech activity from an external microphone near the speech source, providing a performance benchmark, orfrom one of the embedded microphones, assuming a more realistic scenario. Experimental measurements are
performed in a semi-anechoic chamber, with a UAV mounted on a stand and a loudspeaker playing a speech
signal, while setting three distinct and fixed propeller rotation speeds, resulting in three different signal-tonoise ratios (SNRs). The recordings obtained and made available online are used to compare the proposed
method to the use of the standard multichannel Wiener filter (MWF) estimated with and without the propellers’
microphones being used in its formulation. Results show that compared to those, the use of PK-MWF achieves
higher levels of improvement in speech intelligibility and quality, measured by STOI and PESQ, while the SNR
improvement is similar.

Keywords: Speech enhancement, unmanned aerial vehicle, noise reduction

1

INTRODUCTION

The use of unmanned aerial vehicles (UAVs), usually referred to as drones, has not only become more common
in modern society, but also more diverse in terms of its applications and consequently, the digital signal processing solutions explored. In situations where drones are used for cinematography, surveillance and emergency
search and rescue operations, the potential of recording and processing audio signals, as opposed to limiting
the use of UAVs to image and video capture only, has become more evident and researched over the past few
years [1, 2]. A fundamental problem in recording audio with a UAV, however, is the high level of ego-noise
generated by its rotors [3, 4], which interferes with the target source signal and consequently reduces soundquality. In order to overcome this issue, noise reduction and speech enhancement techniques can be employed[5].
In the current state of the art, noise reduction and speech enhancement frameworks based on the standard
multichannel Wiener filter (MWF), computed as a function of the speech source steering vector, or on blindsource separation (BSS) methods, have already been used and tested on UAV setups [2, 6]. However, theseapproaches require the speech source location to be presumably known, which can be a limiting factor inmore practical scenarios. An alternative formulation of the standard MWF allows the filter coefficients to becomputed as a function of the speech correlation matrix instead [7], which is in turn estimated by using speech
activity information obtained with a voice activity detector (VAD) or with speech presence probability (SPP)
estimates [8]. When considering this formulation, an extension of the standard MWF can be realized when

prior knowledge on the noise is available from specific channels of the microphone array and used to improverobustness in the estimation of the speech correlation matrix, resulting in the prior knowledge multichannel
Wiener filter (PK-MWF) [9].
In this paper, a method is proposed for performing speech enhancement using ego-noise references with amicrophone array embedded in a UAV, while assuming that the source location, and consequently, the source
steering vector with respect to the array configuration, is unknown. The ego-noise reference signals are captured
with microphones located near the UAV’s propellers and used to constrain the estimation of the speech correlation matrix necessary for computing the prior knowledge multichannel Wiener filter (PK-MWF). Speech presence probability (SPP) information is estimated in order to identify speech activity in different time-frequency
points. Such SPP estimation can either be obtained from one of the embedded array’s microphones or from anexternal microphone also used for recording the auditory scene and providing a performance benchmark. Experimental measurements, made available online [10], were performed in a semi-anechoic chamber for testing theimplementation of the standard MWF and the PK-MWF. It will be shown that the use of PK-MWF achieves
higher levels of improvement than the standard MWF in terms of short-time objective intelligibility (STOI) andperceptual evaluation of speech quality (PESQ), while the SNR improvement is similar.
The paper is organized as follows. In section 2, the signal model is defined. In section 3, the proposed
method is explained and its formulation is compared with the standard multichannel Wiener filter (MWF). In
section 4, the experimental setup used for the recordings and the audio processing framework are described. In
section 5, the results obtained are discussed and finally, in section 6, a conclusion is presented with a summary
and final remarks on the work accomplished.

2

SIGNAL MODEL

The signal in the short-time Fourier transform (STFT) domain captured by a single microphone with index m
is defined asym (κ, l) = am (κ, l) s (κ, l) + nm (κ, l) ,(1)

where ym is the resulting signal, am is the corresponding transfer function from the target source to the microphone, s is the speech source signal, and nm is the noise component. The indices κ and l correspond to
the frequency bin index and the observation frame index, respectively. In the following equations, we consider
processing the different frames and frequency bins independently, and their corresponding indices will be omitted for brevity. Considering a microphone array with M elements, a signal vector is obtained by stacking all
microphone signal equations, resulting in
y = s+n= as + n,(2)(3)

with {y, s, a, n} ∈ CM , and s ∈ C. Assuming that the desired target source signal and the noise component areuncorrelated, the correlation matrices adhere to
Ryy = E yyH
(4)
 H
 H
(5)
= E ss + E nn
= Rss + Rnn ,(6)where E{.} denotes the expectation operator, (.)H denotes the conjugate transpose, Ryy is the microphone signal
correlation matrix, Rss and Rnn are the speech
 signal and noise correlation matrices, respectively. The speechcorrelation matrix corresponds to Rss = aaH E s2 , and hence it is rank-1 under the common assumption that a
is deterministic.

3

PROPOSED METHOD

We propose to use the PK-MWF with prior knowledge obtained from microphones mounted below the rotorsof the UAV. In section 3.1, we introduce the general formulation of the multi-channel Wiener filter. In section

3.2, we outline the standard MWF realization, and in section 3.3, we describe the PK-MWF realization and its
application in the context of a UAV.
3.1 Formulation of the multichannel Wiener filter (MWF)
Let a target speech reference signal d be defined as
d = eTd s,(7)with ed = [1, 0, . . . , 0]T being a vector selecting the desired source signal component in the microphone array’s
first channel, which is here without loss of generality considered as the reference channel, and (.)T denoting
the transpose.
In the formulation of the multichannel Wiener filter (MWF), it is aimed to estimate d as a linear combinationof all microphone signals in y by minimizing the following mean squared error (MSE) criterion and obtaining
the filter weights w̄ as [7]w̄ = arg min E |d − wH y|2 .
(8)w

If the microphone signal correlation matrix Ryy has full rank, the optimal solution to (8) is [7]w̄ = R−1
yy Rss ed .

(9)Since the speech-only correlation matrix Rss cannot be directly observed from the microphone signals due tothe presence of noise, its estimate can be based on the analysis of the speech activity’s on-off behavior in thetime-frequency domain. While assuming short-term stationarity of y, the estimation of the speech-plus-noise andnoise-only correlation matrices (R̂yy and R̂nn , respectively) can be performed instead and used to estimate R̂ss .
3.2 Standard multichannel Wiener filter (MWF) realization
In the standard multichannel Wiener filter, the speech-only correlation matrix is estimated by solving the following optimization problem [11]:R̂ss = arg min −H−1
R̂nn2 R̂yy − R̂nn − Rss R̂nn2

rank(Rss )=1
Rss 0

2

,

(10)

F

where k.kF denotes the Frobenius norm and R̂ss is constrained to be a rank-1 and positive semi-definite matrix.
The solution to (10) is based on the generalized eigenvalue decomposition (GEVD) of the matrix pencil {R̂yy ,R̂nn } [11]:R̂yy = Q̂Σ̂yy Q̂H ,

(11)

H

(12)

R̂nn = Q̂Σ̂nn Q̂ ,where Σ̂yy and Σ̂nn are diagonal matrices containing the generalized eigenvalues σ̂yi and σ̂ni of R̂yy and R̂nn ,respectively, and Q̂ is an invertible matrix containing the generalized eigenvectors in its columns. The generalized eigenvalues and eigenvectors are assumed to be sorted in descending order of σ̂yi and σ̂ni . Using (6), theestimate of R̂ss can then be obtained as
R̂ss = Q̂ diag{σ̂y1 − σ̂n1 , 0, . . . , 0}Q̂H ,(13)with diag{.} building a diagonal matrix and σ̂y1 and σ̂n1 being the top-left elements of Σ̂yy and Σ̂nn , respectively, yielding the largest ratio between eigenvalues σ̂yi /σ̂ni . Finally, by substituting (13) and (11) into (9), thestandard multichannel Wiener filter can be formulated as [11]σ̂n1ŵMWF = Q̂−H diag 1 −, 0, . . . , 0 Q̂H ed .
(14)
σ̂y1

3.3 Prior knowledge multichannel Wiener filter (PK-MWF) realization
In the standard multichannel Wiener filter formulation, the spatial configuration of the microphone array usedwith respect to the noise sources is not explicitly exploited. However, for a practical setup such as the microphone array embedded in a UAV considered in this study, the prior knowledge obtained from array elements
sufficiently close to the main noise sources interfering in the signals, i.e. the device’s rotors, can be used in an
attempt to provide a more robust estimation of Rss .
We define MS+N as the number of array elements that are assumed to contain speech and noise, and MN
as the number of array elements assumed to only contain noise, or have a sufficiently low signal-to-noise ratio(SNR) such that the speech component is negligible. In a UAV setup, such configuration is here assumed to beattained if each of the MN microphones are placed in close proximity to one of the vehicle’s propellers. The
total number of microphones used are then described as M = MS+N + MN . Let the identity and all-zero matrix
be denoted by I and 0, respectively, where we indicate their dimensions by a subscript. We define the selection
matrix H and the blocking matrix B as




IMS+N
0MS+N ×MN
,
.
H=
B=
(15)
0MN ×MS+N
IMN
Thus, the selection matrix H and blocking matrix B have dimensions M × MS+N and M × MN , respectively. In
the PK-MWF, the following cost function is then minimized in order to estimate R̂ss [9]:R̂ss = arg min −H−1
R̂nn2 R̂yy − R̂nn − Rss R̂nn2

rank(Rss )=1
BH Rss B=0
Rss 0

2

.

(16)

F

The matrix R̂ss to be estimated now, which is the counterpart to (10) in the standard MWF, is not only constrained to be rank-1 and positive semi-definite, but also to have its column and row spaces lying in the column
space of H, such that BH Rss B = 0. The solution to (16) (proof omitted) is obtained by firstly applying alinearly-constrained minimum variance (LCMV) beamformer C to the vector y, defined by the following criterion [9]:
C = arg min trace CH Rnn C
(17)
C

s.t.

HH C = IMS+N ,(18)

which has its solution based on a generalized sidelobe canceler (GSC) [12]:Ĉ = H − BF̂,

(19)

−1 H
F̂ = BH R̂nn B
B R̂nn H.

(20)

By applying Ĉ to y, we obtain a vector with reduced dimension yred = ĈH y. Similarly, we can obtain theH
reduced dimension correlation matrices R̂red
Ĉ and R̂red = ĈH R̂nn Ĉ. Then, a generalized eigenvalue
yy = Ĉ R̂yy
 red rednn
decomposition (GEVD) of the reduced matrix pencil R̂yy , R̂nn is performed as in [9]red H
red redR̂redyy = Q̂ Σ̂yy (Q̂ ) ,

(21)

red H
red redR̂rednn = Q̂ Σ̂nn (Q̂ ) ,(22)

which is the PK-MWF counterpart to (11)-(12) in the standard MWF. Again, we assume the generalized eigenvalues and eigenvectors to be sorted in descending order of σ̂yredand σ̂nred. The matrix R̂ss can then be expressedii
as [9]
 red
redR̂ss = HQ̂red diag σ̂y1
− σ̂n1, 0, ..., 0 (Q̂red )H HH ,(23)

which corresponds to the PK-MWF counterpart to (13) in the standard MWF. Finally, by substituting (23) and(21) into (9), the estimated prior knowledge multichannel Wiener filter (PK-MWF) coefficients are given by
(
)
red
σ̂n1
red −HŵPK-MWF = Ĉ(Q̂ ) diag 1 − red , 0, . . . , 0 (Q̂red )H HH ed .
(24)
σ̂y1

4

EVALUATION

In order to compare the performance of both multichannel filtering methods specified in section 3, experimental
tests were carried out in a semi-anechoic chamber at Siemens Digital Industries Software, in Leuven, Belgium.
In section 4.1, the measurement setup is detailed, and in section 4.2, the processing framework and the different
cases studied are described.
4.1 Measurement setup
In the measurement setup considered for performance evaluation, a MikroKopter MK EASY Quadro V3 (HiSystems GmbH) quad-rotor UAV is mounted on a support stand such that the bottom of its custom-made frameis 1.15 m above the floor. The UAV is equipped with a 16-element array composed of electret condenser microphones, a sound card and a minicomputer for performing audio recordings. A loudspeaker is placed 2 maway from the base of the stand and close to ground level, in order to simulate a scenario of a speech source
being present below the UAV’s line-of-sight. An external microphone is placed 0.2 m above the loudspeaker
for recording the reference speech signal and allowing a performance comparison in the processing stage. A
sketch representation is depicted in Fig. 1, and a photo of the actual measurement setup inside the semi-anechoic
chamber is presented in Fig. 2.
With the UAV’s throttle level fixed to a nearly constant value, which was possible due to the remote control’s throttle joystick not being spring-loaded, the setup conditions could be considered an approximation ofa hovering state for the UAV [13]. A male speech signal from the VCTK corpus [14] was played from theloudspeaker and recorded both by the external reference microphone and the drone’s microphone array. The
recordings, available online [10], were repeated for three different levels of throttle, and therefore, three different propeller rotational speeds, measured at run time with a laser probe placed under one of the propeller
blades.
UAV
UAV<latexit 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External
microphone
External
microphone

1.15 mLoudspeaker

0.2 m
Loudspeaker2m2m(a) Side view(b) Top view

Figure 1. Sketches of experimental setup

4.2 Processing
The measurements were processed offline on Matlab by, firstly, downsampling the recorded signals from 44.1 kHz
to 16 kHz. Then, a 512-point square-root of Hann observation window with 50% overlap is employed for computing the FFT for the signal frames from each microphone. The correlation matrices R̂yy and R̂nn are estimatedUAV

External
microphone
Loudspeaker

Figure 2. Experimental setup in semi-anechoic chamber
for each frequency bin based on the speech presence probability (SPP) [15] value of all frames being processed.
Such SPP can be either estimated from one of the channels from the embedded microphone array (denoted asiSPP) or alternatively from the reference external microphone (denoted as xSPP), in order to provide a performance benchmark in the succeeding analysis of results obtained. We define β (κ, l) as a speech activity
indicator, which is computed as(
1 (speech active),
if SPP(κ, l) ≥ 0.5
β (κ, l) =(25)
0 (speech inactive), otherwise.
This parameter is then used to estimate the desired correlation matrices asR̂yy (κ) =R̂nn (κ) =L1y(κ, l)yH (κ, l)β (κ, l),∑
LON (κ) l=1

1

(26)

L

y(κ, l)yH (κ, l)(1 − β (κ, l)),LOFF (κ) ∑

(27)

l=1

where L denotes the total number of frames processed, and LON (κ) and LOFF (κ) correspond to the total numberof frames for frequency bin κ where, based on the value of β (κ, l) being 1 or 0, the desired speech component
is assumed to be active or inactive, respectively.
In order to observe possible variations in performance according to different microphone array configurations, the MWF and PK-MWF filters are implemented considering three different numbers of channels usedfrom the embedded microphone array, as illustrated in Fig. 3. In the PK-MWF implementation, the number ofmicrophones from the main array used, denoted by MS+N , corresponds to the number of channels employed in
the selection matrix H, whereas the four propeller microphones, placed below each pair of blades and denotedby MN , are always used as noise references in the blocking matrix B. In the case of MWF, however, we consider two implementations where the propeller microphones MN are used in its formulation or not, in addition
to the channels used from the main array MS+N .

The performance of the different methods implemented is evaluated in terms of SNR improvement, as well
as of two other objective measures, namely the short-time objective intelligibility (STOI) [16] and the perceptual
evaluation of speech quality (PESQ) [17].
Propeller microphones (MN )Used main array microphones (MS+N )Unused main array microphones(a) MS+N = 4, MN = 4(b) MS+N = 8, MN = 4(c) MS+N = 12, MN = 4

Figure 3. Illustration of different microphone array configurations considered in the formulation of MWF and
PK-MWF.

5

RESULTSThe resulting SNR improvement obtained at the channel where the reference signal estimate dˆ = ŵH y is computed is presented in Fig. 4. Different numbers of microphones in the proposed filtering formulations and the
SPP estimates were considered. The bottom and top horizontal axes indicate the original SNRs considered andthe corresponding average rotational speed of the rotors in revolutions/min (rpm), respectively. We can observe
that, with the SPP estimate from the reference external signal (xSPP), the SNR improvement is greater thanwhen the SPP estimate from the embedded array’s own reference channel (iSPP) is used, as the external microphone placed near the target source provides a signal with greater SNR itself. Therefore, the detection of
speech activity is more reliable, resulting in more accurate estimates of the necessary correlations matrices here
considered. Since having an external microphone near the target speaker may not be realistic in all kinds ofscenarios, the use of the external SPP estimate can be here seen as a way to provide a performance benchmark
for the proposed methods, with which it is possible to compare and evaluate the improvements obtained andits possible limitations. In this case, it is observed that even with a poorer SPP estimate, all methods are still
capable of improving the SNR of the target reference channel.
In terms of the number of microphones used for performing the multichannel filtering, it is observed that
performance is improved when using more microphone signals, although the differences are more visible whengoing from MS+N = 4 to MS+N = 8 than when going from MS+N = 8 to MS+N = 12. This is possibly due toperformance saturation related to signal model or SPP errors that could not be improved with the increase in
number of microphones used, as well as to the microphone placement of the last four channels included beingaligned with the rotors’ rotational axes, which is considered to be where the noise level is the strongest [4] andtherefore, not providing as much additional information on the target speech signal as the previously included
microphones.
We can also observe that the performance of PK-MWF in terms of SNR improvement is similar to the oneof MWF while employing the same number of microphones (M = MS+N + MN ). However, the matrix reductions
performed in the PK-MWF might favor its usage in terms of computational complexity.
The improvement in speech intelligibility measured by STOI is presented in Fig. 5. It can be observed thatthe performance of PK-MWF with respect to this perceptual measure is particularly better than both formulations of standard MWF considered when employing the SPP estimate from the embedded array’s own reference
signal (iSPP), indicating the advantage of using the ego-noise reference signals as proposed here for improving
robustness to SPP errors which affect the estimation of R̂yy and R̂nn . In addition to having a similar behavior
in performance improvement when including more channels as the one seen in terms of SNR, we can also
observe that the STOI improvement rate increases with the original values from the array’s reference signal,
which corresponds to a decrease in the average rotor speed.
MWF (M = MS+N + MN ) - iSPP
MWF (M = MS+N + MN ) - xSPP

PK-MWF (M = MS+N + MN ) - iSPP
PK-MWF (M = MS+N + MN ) - xSPP

Average rotor speed (rpm)Average rotor speed (rpm)Average rotor speed (rpm)

6000

6000

6000

4000

15

10

5

0
−12

−10
−8
Original SNR (dB)(a) MS+N = 4

5000

4000

15

SNR improvement (dB)5000

SNR improvement (dB)

SNR improvement (dB)

MWF (M = MS+N ) - iSPP
MWF (M = MS+N ) - xSPP

10

5

0
−12

−10
−8
Original SNR (dB)(b) MS+N = 8

5000

4000

15

10

5

0
−12

−10
−8
Original SNR (dB)(c) MS+N = 12

Figure 4. SNR improvement in function of original SNRs associated to different average rotor speeds.
Finally, the improvement in speech quality measured by PESQ is presented in Fig. 6, which indicates a better
performance of PK-MWF compared to the standard MWF filters when using either one of the SPP estimatesconsidered. As opposed to the STOI improvement depicted in Fig. 5, the performance improvement rate in thiscase decreases with the original PESQ score of the reference signal considered, which is in turn associated to
an increase in the average rotational speed of the rotors.

6

CONCLUSION

In this paper, a method for speech enhancement using ego-noise references with a microphone array embedded
in an unmanned aerial vehicle was proposed. The ego-noise reference signals captured by microphones placednear the UAV’s rotors were used as prior knowledge in the PK-MWF for estimating the necessary speech correlation matrix in a constrained optimization problem. Speech presence probability (SPP) was estimated fromboth an external reference microphone and one of the embedded array’s channels in order to detect speech
activity. Results obtained from experimental recordings showed that, especially in a more realistic scenario
where the SPP is estimated from one of the UAV’s own microphones, the PK-MWF implementation provided
greater improvement in speech intelligibility and quality when compared to the use of standard MWF, with andwithout the propeller microphones being included in its formulation. While the SNR improvement is similar
for PK-MWF and the standard MWF using the propeller microphones, it can be argued that employing the
PK-MWF implementation might be advantageous in terms of computational complexity due to the performed
matrix reductions when considering real-time processing applications. Future work includes expanding the mea-surement campaign to consider moving UAVs, updating the correlation matrices over different time frames, andreformulating the noise signal model to consider more particular characteristics of UAVs’ ego-noise.
MWF (M = MS+N + MN ) - iSPP
MWF (M = MS+N + MN ) - xSPP

Average rotor speed (rpm)5000

Average rotor speed (rpm)

4000

6000
0.8

STOI improvement

STOI improvement

6000
0.8

0.6

0.4

0.2

0
0.35

PK-MWF (M = MS+N + MN ) - iSPP
PK-MWF (M = MS+N + MN ) - xSPP

4000

0.6

0.4

0.2

0
0.35

0.4
0.45
Original STOI

5000

Average rotor speed (rpm)
6000
0.8

STOI improvement

MWF (M = MS+N ) - iSPP
MWF (M = MS+N ) - xSPP(a) MS+N = 4

4000

0.6

0.4

0.2

0
0.35

0.4
0.45
Original STOI

5000(b) MS+N = 8

0.4
0.45
Original STOI(c) MS+N = 12

Figure 5. STOI improvement in function of original STOI values associated to different average rotor speeds.
MWF (M = MS+N + MN ) - iSPP
MWF (M = MS+N + MN ) - xSPP

Average rotor speed (rpm)

0.6

0.4

0.2

0

Average rotor speed (rpm)5000 4000

6000
0.8
PESQ score improvement

PESQ score improvement

6000
0.8

1

1.2
1.4
Original PESQ score(a) MS+N = 4

PK-MWF (M = MS+N + MN ) - iSPP
PK-MWF (M = MS+N + MN ) - xSPP

Average rotor speed (rpm)5000 4000

6000
0.8
PESQ score improvement

MWF (M = MS+N ) - iSPP
MWF (M = MS+N ) - xSPP

0.6

0.4

0.2
1

1.2
1.4
Original PESQ score(b) MS+N = 85000 4000

0.6

0.4

0.2
1

1.2
1.4
Original PESQ score(c) MS+N = 12

Figure 6. PESQ improvement in function of original PESQ scores associated with different average rotor speeds.

ACKNOWLEDGEMENTS
This research work was carried out at the ESAT Laboratory of KU Leuven, in the frame of FWO Mandate: SB
1S86520N. The research leading to these results has received funding from the European Research Council under the European Union’s Horizon 2020 research and innovation program / ERC Consolidator Grant: SONORA(no. 773268), the H2020 MSCA ITN ETN PBNv2 project (GA 721615) and the H2020 MSCA ITN ETNVRACE project (GA 812719). This paper reflects only the authors’ views and the Union is not liable for any
use that may be made of the contained information. The authors also wish to thank Mr. Sacha Morales for his
support in the measurement campaign.

REFERENCES[1] Strauss M, Mordel P, Miguet V, Deleforge A. DREGON: Dataset and methods for UAV-embedded soundsource localization. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
Madrid: IEEE; 2018. p. 1–8.
[2] Hioka Y, Kingan M, Schmid G, Stol KA. Speech enhancement using a microphone array mounted on an unmanned aerial vehicle. In: 2016 IEEE International Workshop on Acoustic Signal Enhancement (IWAENC).
Xi’an, China: IEEE; 2016. p. 1–5.
[3] Hubbard HH, editor. Aeroacoustics of flight vehicles: theory and practice. NASA Office of Management,
Scientific and Technical Information Program; 1991.
[4] Hioka Y, Yen B, McKay R, Kingan M. Clean audio recording using unmanned aerial vehicles. In: Unmanned Aerial Systems. Elsevier; 2021. p. 175–202.
[5] Gannot S, Vincent E, Markovich-Golan S, Ozerov A. A consolidated perspective on multimicrophone speech enhancement and source separation. IEEE/ACM Trans Audio Speech Lang Process. 2017
Apr;25(4):692–730.
[6] Wang L, Cavallaro A. A blind source separation framework for ego-noise reduction on multi-rotor drones.
IEEE/ACM Trans Audio Speech Lang Process. 2020;28:2523–37.
[7] Doclo S, Moonen M. GSVD-based optimal filtering for single and multimicrophone speech enhancement.
IEEE Trans Signal Process. 2002 Sep;50(9):2230–44.
[8] Ngo K, Spriet A, Moonen M, Wouters J, Jensen SH. Incorporating the conditional speech presence probability in multi-channel Wiener filter based noise reduction in hearing aids. EURASIP J Adv Signal Process.
2009 Dec;2009(1):930625.
[9] Rompaey RVan, Moonen M. Distributed adaptive node-specific signal estimation in a wireless sensor network with partial prior knowledge of the desired source steering vector. In: 2019 27th European Signal
Processing Conference (EUSIPCO). A Coruna, Spain: 2019. p. 1–5.
[10] Tengan E, Dietzen T, Ruiz S, Alkmim M, Cardenuto J, van Waterschoot T. Replication data for: Speechenhancement using ego-noise references with a microphone array embedded in an unmanned aerial vehicle.
KU Leuven RDR; 2022. Available from: https://doi.org/10.48804/PZAVUC.
[11] Serizel R, Moonen M, Van Dijk B, Wouters J. Low-rank approximation based multichannel Wiener filter
algorithms for noise reduction with application in cochlear implants. IEEE/ACM Trans Audio Speech Lang
Process. 2014 Apr;22(4):785–99.
[12] Breed BR, Strauss J. A short proof of the equivalence of LCMV and GSC beamforming. IEEE Signal
Process Lett. 2002 Jun;9(6):168–9.
[13] Hioka Y, Kingan M, Schmid G, McKay R, Stol KA. Design of an unmanned aerial vehicle mounted
system for quiet audio recording. Applied Acoustics. 2019 Dec;155:423–7.

[14] Yamagishi J, Veaux C, MacDonald K. CSTR VCTK corpus: English multi-speaker corpus for CSTR voicecloning toolkit (version 0.92). University of Edinburgh. The Centre for Speech Technology Research. 2019.
[15] Gerkmann T, Krawczyk M, Martin R. Speech presence probability estimation based on temporal cepstrum
smoothing. In: 2010 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)).
Dallas, TX, USA: IEEE; 2010. p. 4254–7.
[16] Taal CH, Hendriks RC, Heusdens R, Jensen J. An algorithm for intelligibility prediction of time–frequency
weighted noisy speech. Vol. 19, IEEE/ACM Trans Audio Speech Lang Process. 2011 Sep;19(7):2125–36.
[17] Rix AW, Beerends JG, Hollier MP, Hekstra AP. Perceptual evaluation of speech quality (PESQ) - a new
method for speech quality assessment of telephone networks and codecs. In: 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing Proceedings (ICASSP). Salt Lake City, UT, USA:
IEEE; 2001. p. 749–52.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022, in Gyeongju, Korea
Extended abstractABS-0148

Sound Quality Index for Assessment of MDPS Operating Noise
Kang Hyun An1; Sang Kwon Lee2; Kyung Hwan Park3; Jong Ho Park4; In Je Cho5
1,2

INHA UNIVERSITY, Korea

3,4,5

HYUNDAI MOBIS, Korea

ABSTRACTThe paper presents a sound quality index which is used for evaluation of subjective perception of the soundemitted from bearings or motors of a motor driven power steering (MDPS) system in passenger car. The
index is developed based on sound metrics psychoacoustic parameters such as loudness, sharpness, roughness,and tonality. A special tonality for evaluation of motor sound of MDPS system is also developed. The jury
test is performed for the subjective evaluation of MDPS sounds. 10 MDSPS sounds are used for the subjective
evaluation. Thirty persons were attended to the listening test of 10 MDPS. To select a subjective query forsound quality index development, various subjective expressions that can represent operating noise areinvestigated. Semantic different method is used for the query selection test. A query is selected through factor
analysis of subjective results. As for the results of the subjective evaluation, the average value of results is
calculated through statistical processing such as box whiskers. A sound quality index is developed by
selecting sound quality parameter that are highly correlated with subjective evaluation values among
Loudness, Roughness, Fluctuation Strength, Sharpness, and Tonality.
Keywords: MDPS, Sound Quality

1. INTRODUCTION
As the use of electric and hybrid vehicles increases, noise masked by engine noise is becoming an
issue. Among them, the noise emitted from the MDPS connected to the steering wheel into the interiorof the vehicle makes people uncomfortable. In order to evaluate the discomfort people feel andimprove sound quality [1], many studies are being conducted to evaluate sound quality objectivity.
Even if it has a similar sound pressure level value, the emotional part that a person feels varies
depending on the noise characteristics. Therefore, noise analysis using psychoacoustic parameterssuch as loudness, roughness, fluctuation strength, sharpness and tonality is necessary [2]. This study
uses psychoacoustic to find parameters that are most correlated with the evaluation people feel and topresent an index that can be evaluated objectively.

2. Sound Quality Analysis of MDPS
2.1 Subjective evaluation
To develop sound quality index, subjective evaluation was performed for 10 MDPS. The noise wasrecorded on the Biaural Acoustic Head for 10 seconds with MDPS operating at the same RPM in the
dynamometer in the anechoic chamber. The subjective evaluation was conducted by 35 students andthe Semantic different method was used. Subject rating ranges from five levels: 1 (very bad), 2 (bad),
3 (normal), 4 (good) and 5 (very good) [3].

ankh4059@naver.com
sangkwon@inha.ac.kr
3
jongho0201@mobis.co.kr
4ijcho@mobis.co.kr
12

Figure1 – Comparison of subjecting rate between MDPS Sound
Figure1 shows the result of the subjecting rate for Pleasant. The middle of the bar represents theaverage. The vertical bar represents the variance. S2 and S8 with large loudness were evaluated very
badly. On the contrary, in the case of S3 and S5, it is the sound that people felt Pleasantly.
2.2

MDPS Sound MetricFigure2 – Correlation between Subjective rating and psychoacoustic parameters
Figure 2 is a graph showing the Pearson’s correlation coefficient (R) [4] of subjective evaluationand sound quality elements. Sound quality elements other than tonality have an inverse correlation.
Loudness and Sharpness have a high correlation (R>0.9). but Fluctuation Strength and Tonality havea low correlation (R<0.3). We decided that Loudness, which has the highest correlation with the
Pleasantness of MDPS, is metric.
As a result of the regression of 10 MDPS sounds, sound Quality index is(1)
𝑷𝒍𝒆𝒂𝒔𝒂𝒏𝒕 = 𝟖. 𝟒𝟑 − 𝟎. 𝟓𝟓𝑳𝒐𝒖𝒅𝒏𝒆𝒔𝒔

Figure3 – relation between subjective rating and objective rating
Figure 3 is the correlation result of subjective rating and objective rating. Pleasant index exhibit
correlation coefficients of 𝑅 2 = 0.94. The index is used to evaluate pleasant for new MDPS.

3. CONCLUSIONSThis paper provides an MDPS sound quality index. Pleasant was selected as a query language that
expresses the sound of MDPS well, and 35 students were conducted. The sound metric was used todetermine the correlation between subjective evaluation and MDPS noise. Representative metrics suchas loudness, sharpness, roughness, fluctuation strength and tonality were used. as a result, the mostcorrelated factor with psychoacoustic metric was loudness. The index becomes a guideline for
improving the sound quality of the new MDPS.

ACKNOWLEDGEMENTS
This work was supported by the National Research Foundation of Korea (NRF) grant funded by
the Korea government (MSIT) (No. 2022R1F1A1062889). This work was supported by the Hyundai
Mobis.

REFERENCES
1. J. Back, S.-K. Lee, S. M. Lee and K. An, Design and implementation of comfort-quality HVAC soundinside a vehicle cabin, Applied Acoustics, 2021;177:107940.
2. Kang H-S, Cho H-Y, Lee S-K, Shon J-H. Development of an index for the sound and haptic quality of aseat belt. Applied Acoustics, 2015;99:145–54.
3. Otto N., Amman S., Eaton C., Lake S., 2001 Guidelines for Jury Evaluations of Automotive Sounds.
Sound and Vibration, 2001;35:34-47.
4. Robinson M. Symmetry and the Standard Model: Mathematics and Particle Physics. New York: Springer;
2011.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022 in Gyeongju, Korea

ABS-0476

Comparison of Active Sound Design methods in the context ofSound Quality
Markus BODDEN1; Torsten BELSCHNER1; Jeroen LANSLOTS2
12

neosonic, Germany

Siemens Digital Industries Software, Belgium

ABSTRACT
Active Sound generation for electric vehicles is substantially different from Active Sound generation for
combustion engine cars. While the second requires to complement and accomplish the sound inherently
generated by the internal combustion engine, the first demands a full synthesis of the sound due to the lackof prominent electric motor sound. A variety of different methods for this full synthesis have been developedin the past years, starting from pitched playback of sound samples, FM-synthesis from musical applications,extended order-based synthesis up to advanced granular synthesis. All these methods have their specific pros
and cons with regard to the requirements to implement them into embedded hardware for mass-production,
but also with regard to capabilities and perception of the resulting sound and thus Sound Quality related
aspects.
The different methods will be discussed and put into the context of the various requirements. Examples ofsound generation for different applications will be given.
Keywords: Active Sound Design, Sound Quality

1. INTRODUCTION
While Active Sound Design (ASD) is well established and commonly used for vehicles withinternal combustion engine (ICE) the application to electric powertrain vehicles (EV) has only beenslowly progressing. Although the first EV with interior ASD in mass production was introduced in2012 2, it took some time before the next vehicle models followed in 2018 3. Currently more and more
EVs use this technology, which also got boosted by the legislative requirement to add a pedestrian
warning sound (AVAS) to the exterior for most regions.
In contrast to the application for ICE vehicles, where ASD is used to add some components to thegeneric ICE engine sound, for EVs basically the complete powertra in sound needs to be synthesized
integrating the residual tonal components of the EV. The task thus is not to refine an existing sound,but to create a new sound from scratch.
Building a sound from scratch allows a high degree of freedom of design, and as a consequence avariety of different sound generation methods can be used. These methods al have their specific pros
and cons with regard to their features, available parameters, achievable sound variability and character,but also with regard to the requirements for embedded solutions to be used for mass production in thevehicles.

2. SOUND QUALITY ASPECTS OF ACTIVE SOUND DESIGNSound Quality for automotive applications has been and still is a topic of numerous research
activities. Several definitions of Sound Quality have been proposed, e.g., in (1) by Blauert and Jekosch,pointing out that the fulfillment of the task of a sound needs to be evaluated .
Sound Quality in the context of Sound Design even is more complicated. Sound Design
incorporates an aesthetic process which require different criteria compared to “traditional” Sound
1
2
3

info@neosonic.eu
Mercedes AMG SLS ED
Jaguar I-PaceQuality in the automotive sector. Metrics that have been developed in the past decades can partly beapplied to evaluate specific aspects of the generated sound, but not to evaluate the quality of the Sound
Design (e.g., Lanslots et al (2)).
The quality basically is composed of 2 aspects, the Audio Quality of the used sound generation
algorithms, and the Sound Quality of the generated sounds.
The first aspect thus mainly comprises technical criteria defined by the toolchain used, the second
one evaluating the end result of the creative process, so what has been done with the toolchain – thisdepends on the individual result of the design process.
In order to evaluate this result of the design process, the fulfilment of the task of the sound has tobe evaluated according to the definition of Sound Quality. In this context the following tasks mightneed to be considered:• give the driver feedback about the current status of the vehicle• increase the interaction of the driver with the vehicle• increase the emotional expression of the vehicle• implement a Brand Sound• allow personalization• implement a “sound floor” and mask other component sounds which might be annoying
Most of these tasks are rather abstract and hard to link to physical attributes, while the last point
has a direct lrelation to standard NVH Sound Quality, as Active Sound can be used to reduceannoyance and increase pleasantness.
The final Sound Quality achieved with Active Sound Design thus is based on two main categories– the technical basis in form of the available Active Sound Design methods includi ng the interface tothe user on the one side, and the defined target (task) and result of the creative process of the Sound
Designer on the other hand.
The first aspect can be discussed and assessed in a general way, while the second depends on theindividual application scenario and boundaries. This paper will focus on the first category, discussing
the different methods to create active sounds in the next chapter.

3. ACTIVE SOUND DESIGN METHODSIn general, any sound synthesis method can be used to generate dynamic vehicle sounds as long as
their synthesis parameters are coupled to the dynamic vehicle data. The most common methods are
discussed in the following paragraphs.
3.1 Order based synthesis
Order based synthesis is the most common method that had been app lied first to support the soundof internal combustion engines (e.g. (3)). In this specific application the task was not to generate acomplete vehicle sound, but to extend an already given sound originating from the internal combustion
engine. The main task was to increase the sportiness of the resulting sound.
Order based synthesis tries to replicate basic acoustic attributes of a combustion engine. Due tothe operation mechanism of the engine with cylinder firings in a periodic process and a period definedby the number of cylinders and the engine rpm, main components of the combustion related sound areenergies at frequencies defined by engine orders. Engine orders are replicated by sine generators withfrequencies defined by the engine order and the current rpm. A bank of these generators thus forms
the order-based synthesis skeleton.
Nevertheless, pure sine wave generators just coupled to engine rpm result in a rather static andsynthetic sound compared to a real combustion engine sound. These basic methods can only be applied
if the generated sound is rather of low level compared the sound generated by the ICE and does not
try to overlay a too different character. Otherwise, the generated components easily stick out andcreate an artificial sound impression.
A main aspect of order-based synthesis algorithm performance with regard to Sound Quality is theachievable authenticity. High authenticity even at high levels and high sound character changes
require specific extensions found in advanced order-based methods which have been proposed in (4)and (5).
• The phase relation between the different orders has a crucial impact on the resulting soundcharacter. Due to a lack of systematic understanding of the phase relation typically random
phases are used• rpm data typically is taken from the CAN bus and results in rather static engine orderfrequencies. In contrast to that the combustion process itself is not perfectly periodic, as several
impulsive excitations occur per revolution of the engine. The rpm used by the sound generation
algorithm thus is overlayed by a microstructure of rpm variations. This needs to be consideredin the processing• several order sets can be used as a function of dynamic vehicle data, e.g. , the engine load. The
sound generated by an ICE clearly changes its character as a function of load, resulting in anorder setup that depends on load. This can be reproduced by using several load maps which areinterpolated based on the actual dynamic vehicle parameter• order-based synthesis does not mimic other ICE sound aspects (mechanical noise, exhaust, …),
and it is hard to reproduce the “dirty” character of a sporty combustion engine with a limited
number of orders. Specific after-processing thus can be applied to enhance the generated sound.

Figure 1 – order data (order level vs engine rpm) implementation in different maps that can be controlled
by dynamic vehicle parameters, e.g., engine load
As the resulting Sound Quality of the basic order-based synthesis is limited, these methods are
typically not applied to EVs. But, using the advanced order-based synthesis, also a complete synthesisof vehicle sound and thus an application to EVs has become possible.
As order-based synthesis is more analytically than design driven the methods typically are used toreplicate already existing sounds or at least to create sound with strong remedies to already existingsounds. That can either be ICE sounds, but also electric motor sounds which can also be described inform of engine orders, but of course a completely different order layout. But, using the specificextensions described above, even order-based synthesis can be used as a creative tool.
Category:• more analysis-related method – more intended to reproduce analysis results
Typical parameters:• number of orders reproduced• order levels as a function of engine rpm (and engine load for advanced implementations)• order phases• random processing and specific after-processing for advanced implementations
Pros:• the method is very predictable as the generated sound quite directly defined by theparameters• the design approach is quite analytical, so a lot of knowledge on ICE sound perception canbe used to design the sound - the method is well suited for NVH engineers as they work withengine orders since decades• a single layer already allows to create variable sound (using advanced implementations)• the implementation is resource-friendly with regard to memory neededCons:• the achievable sound character is more limited
3.2 Pitched Playback synthesis
This sound generation method uses a sound sample that is played back in loop mode to create acontinuous sound. In order to make the sound follow dynamic parameters the pitch can be shifted up
or down, e.g., as a function of vehicle speed or motor rpm.

Figure 2 – Basics of the implementation of pitched playback with control parameter
In order to make this work basic constraints have to be considered regarding the sound sample. Asa generic requirement the sample needs to be of constant character, thus not including any change
within the sample length. As the sample is continuously repeated, the resulting playback would
otherwise result in a sound which has a clear monotonic repetitive structure. In consequence, theresulting sound can just reproduce a single sound character pitched up or down , so the dynamic
variability is limited.
Category:• replication-related method – intended to reproduce something existing
Typical parameters:• sample• pitch at a function of dynamic control parameterPros:• most predictable method as the generated sound is mainly defined by the sound sample used
• easy to reproduce desired or already existing sound features• resource-friendly with regard to required MIPS for embedded implementation• does not require deep Sound Design knowledgeCons:• limited character change as a function of vehicle dynamics as the base character is always
present – the resulting character is rather static• a single layer typically is not sufficient, multiple layers are needed to generate a sufficiently
complex and variable sound• constraints to the sample need to be considered• highest memory resources of all methods required for embedded implementation, dependingon the number and sizes of the samples
3.3 Frequency Modulation synthesis
Frequency modulation (FM) synthesis has commonly been used in synthesizers for music
generation as proposed by Chowning (6). The frequency content of a carrier sound signal is modulatedwith a modulation signal. More complex implementations can use several layers of modulation,
meaning that the output of the first FM modulation stage is fed into a second one and so on. Using
this staggering complex sounds can be created using resource-friendly processing.
The input of FM synthesis can either be an oscillator or a sound sample. For the usage of periodic
sounds (like sines, triangles etc.) just one sound period needs to be in the sound sample.

Figure 3 – FM synthesis example – base signal (200 Hz sine) to cascaded modulators frequency contents

Category:• synthesizer style method, creative sound generation
Typical parameters:• sample for base signal (or oscillator type)• modulation frequency (frequencies)• modulation index (indices)
Pros:• common for users familiar with synthesizers from music generation• resource-friendly with regard to memory requirements (very short sound sample)• resource-friendly with regard to MIPS requirementsCons:• a single layer typically is not sufficient for variable and rich sound generation (dependingon the number of modulations stages)
3.4 Granular synthesis
Granular synthesis is a sample-based sound generation method which – in contrast to pitched
playback – includes specific processing and strategies to make the sound more variable. Granularsynthesis describes just the basic principle of the sound generation method, and implementations in
the different sound generation tools can be quite different.
A sound sample is used as a basis, but from that sample only specific sample segments, the grains,are used for the synthesis. The grains are derived by windowing a region of the sound sample, and areplayed back in loop mode. Typical applications use a set of these grains with a temporal shift in
playback and random position and length addons, resulting in rich, dense, variable and vivid sounds.
In contrast to the pitched playback the sample can include strong character changes, plus it allows
to make the resulting sound even more variable as the position of the grains can be changed as afunction of the dynamic vehicle parameters and profits from variations in the sample used.

Figure 4 – Basics of the implementation of granular synthesis
Typically, samples of a length of just 2s are sufficient to create quite variable and vivid sounds.
Although the synthesis is based on a sound sample, the created sound can – depending on theparameters used – be quite abstracted from the original sample. Due to the more complex soundgeneration even heavy changes e.g. of the pitch can be applied, which can heavily change the charactercompared to the original sound sample.
Compared to pitched playback granular synthesis can be controlled by much more parameters,
which make this method more the creative method. The basic sound generation scheme is less intended
to reproduce an existing sound, but more to create a something new.
Category:• creative design-related method – intended to create something new
Typical parameters:• sample• pitch• number of grains• window function• grain position• grain length• position and length random values
Pros:• creation of rich, vivid and variable sounds even for constant dynamic vehicle parameters.
• creative design tool – the focus is to use this as an instrument to create a new aesthetics• a single layer already allows to create variable sound• resource-friendly with regard to memory requirements as a short sound sample (typicallength ~2s) is sufficient to create a rich and variable sound• only medium MIPS requirements
Cons:• less predictable sound generation (which not necessarily is a con)
3.5 Shepard synthesis
The sample-based methods (pitched playback and granular synthesis) typically shift the content tohigher pitches with higher speed or motor rpm. This leads to the effect that a lack of low frequencies
occurs at higher speed. Furthermore, typically electric vehicles only use a one-gear system, whichmakes the sound much less variable for a run-up.
To overcome this, the effect introduced by Shepard (7) can be used. Tonal components are shifted
up with the dynamic control parameter, but once they exceed a defined upper frequency limit they
move back to the lower start frequency limit. Several of these components with a relative temporal
shift can be used to enhance the richness of sound sensation. These components create the illusion ofan endlessly increasing pitch while staying in the defined spectral range. The Shepard synthesis is a
powerful add-on to other methods described above.

Figure 5 – Shepard with 4 partials from 30 to 200 Hz for a linear control parameter increase vs time(here speed)
Category:• specific add-on method – intended to complement other methods
Typical parameters:• number of components• min and max frequency limits• pitch shift slopePros:• efficient to keep low frequencies and include variability into the sound• resource-friendly with regard to memory requirements as no sound sample is used
• resource-friendly with regard to MIPSCons:
• a single layer typically is not sufficient for variable and rich sound generation

4. APPLICATION TO VEHICLESThe previous chapter has shown that a variety of methods with different characteristics, pros andcons is available. As the choice of the method has an impact on what can be achieved with regard to
Sound Design, the ability to use the methods in a flexible manner is a prerequisite for efficient andsuccessful Sound Design.

Comprehensive Active Sound Designs typically use a combination of the above methods in such a
way that the different methods complement each other and profit from the synergy of the respective
pros and avoiding the respective cons.
A corresponding flexible scheme for vehicle implementation is shown in fig. 6.

Figure 6 – Layout of a flexible Active Sound Generation System
Different sound generation methods can be used in parallel, controlled by a single central unit
which also includes the interface to the user to design and tune the sounds. Typically, the generated
sounds are reproduced using the interior audio system, but also exterior speakers (apart from the
mandatory AVAS speaker) get more popular. Other reproduction systems like shakers (including
vibration feedback) can be used in addition.
The mandatory AVAS sound can either be generated by a separate unit, or deriv ed as a separate
channel from the combined synthesized sound. The latter option ensures that AVAS andinterior/exterior sound generation fit to each other and do not interfere, implementing an important
aspect of Sound Quality for active sounds. The AVAS speaker can then either be an autarkical active
speaker or a passive speaker directly driven by the audio system.
A corresponding application scenario is shown in fig. 7. A sophisticated Sound Design tool offers
an identical interface to the user, independent on the sound generation device used (PC, dedicatedhardware or mass production hardware), and allowing identical performance in all applications
scenarios like laboratory, simulator or real vehicle development environments.

Figure 7 – Toolchain of a Sound Design System as used in (8)Apart from the methods and tools used, the sound designs for mass production applications at
OEMs have to be created in a new systematic approach that integrates the creative part with thestandard NVH process, marketing and user-oriented topics. A corresponding new concept to performthe creative work to vehicle implementation in a guided stringent process that eases Brand Sound
deployment and vehicle segmentation is presented in (9).

5. CONCLUSIONSThe process to design sounds for todays’ vehicles requires to redefine and extend the definition ofSound Quality. The application of vehicle sounds has evolved from pure physical evaluation via theconsideration of the perception of humans to the inclusion of now needed creative processes. Sounds
are no longer only shaped, but synthesized and composed. A variety of different sound generation
methods are available to realize this, and the respective capabilities and requirements need to beconsidered to form a successful Sound Design.

REFERENCES
1. Blauert J, Jekosch U. Sound-quality evaluation - a multi-layered problem, Acustica/acta acustica 83(1997).
2. Lanslots J, De Ryck L, Oltarzewska A, Bodden M, Belschner T. Sound Quality of Interior Active Sound
Enhancement. Internoise 2020, Seoul, Korea.
3 Spannheimer H, Freymann R. Geräuschgestaltung im Fahrzeug mit aktiven Systemen. Fortschritte der
Akustik, Daga 2001, Hamburg
4 Bodden M, Belschner T. Comprehensive Active Sound Design. Part 1: electric and combustion vehicles.
Proceedings of the Internoise 2014, Melbourne, Australia.
5 Belschner T, Bodden M. Comprehensive Active Sound Design. Part 2: operational sounds and Brand
Sound. Proceedings of the Internoise 2014, Melbourne, Australia.
6 Chowning J. The Synthesis of Complex Audio Spectra by Means of Frequency Modulation. Journal of
the Audio Engineering Society. 1973; 21 (7), p. 526-534.
7 Shepard, R N. Circularity in Judgements of Relative Pitch. Journal of the Acoustical Society of America.
1964; 36 (12): p. 2346–53.
8. https://www.plm.automation.siemens.com/global/en/our-story/customers/hyundai/102508/
9. Belschner T, Bodden M. Back to the Future of EV Sound Design. From Imitation to Gravity Maps. ICA
2022, Seoul, Korea.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022 in Gyeongju, Korea

ABS-0477

Back to the Future of EV Sound Design.
From Imitation to Gravity Maps
Torsten BELSCHNER; Markus BODDEN
1neosonic Germany

ABSTRACT
Active Sound Design in Electric Vehicles is construed as an emerging new medium. OEMs, fully inline with the history of new media, in their search for content direction have with very few exceptions
opted to imitate the sound of EVs’ technological predecessors, mostly the imaginary vehicle sounds
from science-fiction movies. In the phase of maturing EV sound design, manufacturers, again fully inline with history, will have to look beyond imitating and have EV become a sound medium in its own
right. Real-time sonification of Hyperobjects for the driver’s information and awareness seems themost promising candidate at this time.
From a conceptual point of view the Active Sound generation approach allows to establish a unique brandsound consistent across an OEM’s sub-brands, vehicle segments, and variants. To warrant sonic consistency
an OEM-wide strategy is devised with all an OEM’s vehicle sounds being derived from common rulesets.
In previous work (1) we presented a rule-based approach for a generic description of acoustic attributes togenerate a brand’s sound. This approach was extended to the multidimensional Gravity Maps Concept whichgoverns a brand’s global soundscape hierarchically. It organizes the sonic sub-brand positioning all the way
down to vehicle segment- and vehicle-level to form a consistent OEM-wide sound domain.
Keywords: Active Sound Design, Sound Quality, Media Theory, AI, Meta-Editors, Gravity Maps

1. MEDIUM
Over the last 150+ years many new technical media have been introduced, e.g. photography, film, CD,music streaming. Electric vehicles (EVs) with their increasingly sophisticated Active Sound Design(ASD) capabilities are considered a new medium, too. EVs are ubiquitous, they sport real-time soundsynthesis, and they are positioned in public spaces.
In this paper the notion of media is generally understood as an apparatus which is accepting input toproduce output with this transformation being governed by a given medium’s specific inherent logic.
This input processing rules are the prevailing factors shaping the output to the point that the input
serves as the mere vehicle to exemplify the rules up to the point that the rules turn out to be the actual
content. Media theorist Marshall McLuhan famously condensed media’s inner workings into the wellknown phrase The medium is the message (2).
Now that the medium ASD for EVs is here, what to fill it with? Traditionally, new media start out by
simply imitating their predecessor.

2. IMITATIONThe content of one medium is always another medium as Kittler has pointed out (3). A few example
arrays of newly emerging media imitating their direct predecessor:! digital camera ← analog camera ← oil on canvas painting! streamed film ← TV film ← analog cinema film ← theater play ← novel (book) ← typescript← live oral history! streamed radio ← analog radio broadcast ← live concert! streamed music ← MP3 player ← Walkman ← analog LP ← live concert

1

tb@neosonic.eu

For ASD in EVs history has repeated as the same procedure has unfolded as it has in previous new
media. Three different types of imitation can be observed in EVs up to 2022:
1. Simulating real-world vehicles in a naturalistic or iconic way.
Sound-alike designs include one or more of combustion engines, jet engines, electric motors.
2. Simulating combustion engines’ physical behavior.
Sound is shaped following a behavioral model of ICEs’ dynamics.
3. Simulating imaginary vehicle sounds showcased in science-fiction movies.
Sound design turns to the realm of phantasy for orientation.
To make existing exterior and interior EV sound designs comparable the following semantic criteria
are proposed by which candidates can be classified and measured on a scale from 0 to 6.
! Simulates real-world vehicles, naturalistically or iconically! Simulates IC engine behavior! Simulates imaginary sci-fi vehicle sound! Uses reference-free abstract sound, e.g. geometric wave shapes! Creates new vehicle sound gestalt! Tonal (0) to noise (6) ratio! Well-tempered (0) v. free (6) pitches
2.1 Simulating real-world vehicles in a naturalistic or iconic way
EV sound designs that fall into this category imitate various real-world engines’ sounds in the waythat they appear. Imitation can occur in a natural way, where the EV’s sound is a close replica of anengine sound, or in an iconic, typified way where any individual traits of an original sound source are
abstracted away towards a generalization.
For imitating combustion engines as part of their sound signatures these EV have been identified andanonymized here to preempt brand bias:! ICE 1 interior! ICE 2 interior! ICE 3 exterior
For imitating jet engines as part of their sound signatures these EV have been identified:! Jet 1 interior! Jet 2 exterior
Put into the classification yields:

Figure 1 – Sound signature classification for EVs imitating real-world engines

2.2 Simulating combustion engines’ physical behavior, not sonic appearance
EV sound designs in this category do not strive to imitate real-world sounds at face value, but go one
step further. As in mathematical derivations they arrive at the actual sound from the sonic modulationof an engine’s behavior while modulating a different sound gestalt from that of an ICE.
To our knowledge there is up to now just one EV which is employing this advanced and complex way
to construct a sound design which follows the known dynamics of an IC engine without duplicating
its sound.

Figure 2 – Sound signature classification for an EV imitating real-world engines’ behavior
2.3 Simulating imaginary vehicle sounds from science-fiction movies
EV sound design concepts in this category have turned their backs on reality and, while being perfectly
real themselves, imitate the sound of fictitious automobiles displayed in futuristic movies. With movie
sound track composers being free of any restrictions to make a sound design work under real-world
conditions, more often than not just showcasing a few seconds of sound, the possibilities for future
car sound designs are virtually phantastic.
Despite the added challenge of having to make imaginary sounds bearable and actually work in thelong term the majority of automotive sound designers in the past 10+ years has embarked on this routeto have their EVs imitate the sonic appearance of cars that aren’t actually existing.
Creating something that lacks a direct reference is a typical trait of the science-fiction genre. Looking
at or hearing something unknown is precisely the hook which is giving these creations their futuristic,alien touch. With a nod to semiotics, the science of symbols it is worth noting, that a real-world
imitation of a science-fiction car’s non-referential “futuristic” sound does in turn have a reference,
which is that of the imitated movie car (4).
Science-fiction movie vehicles that have been found to be particularly influential on EV sound design
include in the order of release:! Tron1 (1982): Light Bikes! Back to the Future I (1985): DeLorean Time Machine! The Wraith (1986): Alien's Futuristic Muscle Car! Total Recall (1990): Hover Cars
! Star Wars Episode I (1999): selection of Podracers! Minority Report (2002): Magnetic Cars
! Batman, The Dark Night (2008): Batpod! Tron 2 Legacy (2010): Light Bikes
Examples for EV ASD sound designs recurring in one or more of their components to science-fiction
movies (selection, also anonymized to preempt brand bias):! EV 1 interior –– Star Wars I, blend of Podracers! EV 2 interior –– Tron 1, yellow + blue Light Bikes! EV 3 exterior –– The Dark Knight, Batpod! EV 4 exterior –– The Wraith, Alien’s car; jet from The Dark Knight, Batpod! EV 5 exterior –– Back to the Future, DeLorean Time Machine

Figure 3 – Sound signature classification for EVs 1–5 imitating vehicle sounds from science-fiction movies

3. BESIDE IMITATION
So far three paradigms in EV sound design could be identified, all of them employ one kind of
imitation: sound of engines, behavior of engines, sound of vehicles from science-fiction movies. As
imitation may not reflect and take full advantage of the new generation of vehicles this could be thetime to actually get back to the future and provide EVs with a sound that represents their position inan already long history of automotive achievements and break-throughs. Which alternatives are there
beyond playing imitation games?
Yet unused EV sound design approaches include, but are not limited to, the following possibilities.
3.1 IronyAt first sight, the Fiat 500e exterior (5) might be interpreted as a superb example of finely measured
irony. On accelerating after ignition, the car’s AVAS system blends a sung imitated ICE rev-up soundinto a rich chord of symphonic film music which has been taken from Federico Fellini's 1973 movie
Amarcord. While the sound creators’ intentions seem otherwise (6) the symphonic blend-in may in an
act of productive misunderstanding be interpreted as being fully indifferent to the imitation game mostOEMs have been pursuing in the last years.
Other examples of using irony to good effect could be sounds not fitting into the natural EV habitat,
which in the majority of cases will be the urban street. Sounds from nature or the ecclesiastic domain,
boldly displayed with a self-assured knowing to be out of context might serve as viable alternatives
to simulation.
3.2 The Master’s Voice – self-referential sound designs
Making reference to persons in the limelight might serve as an alternative and be appealing to many.
Tapping on the fame of pop stars and other celebrities can be used to create a class of revered sounddesigns. This class would not reference the sound of other vehicles or resort to context displacement
and the inversion of meaning as in irony. Instead, it would create sound tracks that reference only
themselves by virtue of their undeniably touted creator’s fame. These sounds will be considered great
not because of their smoking sportiness or movie resemblance, but will work solely by the link tofame or cult-status of their creators.
The process of accustoming drivers to the method of self-reference can be started by famous
Hollywood composers building on their well-known blockbuster sound tracks to create EV sounds.
The process could be continued by enlisting famous pop-musicians who create or license drivableversions of their top 10 hits’ hook lines. The self-referential paradigm might culminate and terminate
in social media influencers authoring sound designs that bear their very signature and express theirtreasured likes and dislikes.
3.3 Mash-up sound signatures
As a third alternative in EV sound design mash-ups might serve. Mash-ups are mixtures and fusionsof seemingly disparate, hitherto unconnected elements. Mashed-up elements gain fresh andunexpected meaning by their being positioned and assembled in unseen ways. Examples may beconstructed from the mix of musical styles, hip-hop, classic, jazz, pop, modern music etc. from which
drivable sound signatures are created, possibly accessing the driver’s music collection for inspirationand on the fly unexpected mashing. AI techniques such as deep learning and pattern recognition may
serve quite well in this approach playing out their particular strengths.

4. BEYOND IMITATION. TOWARD THE EV’S UNIQUE SOUND TRACK
In the previous section we gave examples how new media always started out by being filled with their direct
predecessors’ content and showed this to also apply for ASD in EVs. Predecessors are imitated before
capitalizing on new unique properties they happen to also bring along. An example is the capability of highspeed capturing in digital photography which enables phased renderings of a scene by superimposing
individual frames of an athlete performing a feat. Such assembled pictures provide new insights in motion
study which were impossible before and which make digital photography an indispensable tool in this field.
What are the unique properties of EVs, what are the necessary next steps in EV sound design?
Looking beyond the machine-, behavioral-, and dream factory imitation paradigm EVs are:! ubiquitous! networked! capable of powerful real-time sound synthesis! present in public spaces
In recent years a new object class of Hyperobjects has been proposed by philosopher Timothy Morton (7).
Hyperobjects have vast extensions in space and time, so huge or long-lasting that humans with their limited
sensual, cognitive, and life-span resources are only able to perceive tiny fractions of them.
Examples comprise:! dynamic objects: climate, gravity! man-made objects: global warming, geological sediments such as carbon-, plastic-, nuclear isotopelayers covering the earth
EVs' sound resources are ideally suited to make Hyperobjects hearable and make them a valuable faithfulpart of the everyday driving experience.
EVs' resources may be well used for the sonification of Hyperobjects. The designer creates sounds that
express vehicle and driver being part of one or more existing real-world Hyperobjects in real-time.
Renderings may include:! climate, expressing the EV being part of one or more climate-related Hyperobjects! gravity, exemplifying physical Hyperobjects! CO2 footprint, exemplifying the current contribution to man-made Hyperobjects! traffic in the vicinity, exemplifying the man-made Hyperobject traffic! weather, exemplifying a situational Hyperobject

5. HIERARCHICAL AUTHORING: HARNESSING PROLIFERATION
The process of EVs coming to terms with the range of their possibilities brings along an increasing
amount of complexity for the sound designer. Not only are scope, customer expectation, andinformation content of post-imitation EV sound signatures larger than ever before. In addition, therange of vehicle models any modern OEM has on offer has been steeply increasing in tandem with
the number of model variants also going up, resulting in exponential variant growth.
The three factors have made the sound designer’s life more complicated by the year, as ever moresound signature variants need to be managed and kept consistent across sub-brands, vehicle segments,
models, model variants, and a handful of sound variations in each model for the driver to choose from.
Obviously, this non-trivial complexity is bearing the imminent risk of the sonic appearance fracturing
and falling apart, thus losing the needed cohesion that is indispensable for the recognizability of agreat brand.
5.1 Structuring the OEM sound signature portfolio
To lay the ground work an OEM’s entire range of sub-brands, segments, models, model-variants isorganized in a hierarchical tree which will be utilized as the GUI to directly navigate the soundsignature classes and content. Sound designers enter the domain to work on by accessing this global
tree map. Sound content can be shifted, copied, and organized across branches. The time-saving
content management functions come in handy during creation, assembly, and delivery of soundsignatures by having access to all content from one single point, e.g. for quick sound comparisons ofadjacent vehicle segments or between sub-brands.

Figure 4 – OEM sound signature hierarchy overview
5.2 GUI Meta-Editor Leverage
To tame this pandora's box, leveraged tools come to the rescue. Graphic Meta-Editor approaches that
employ high-level multi-parameter editing offer a start. The model of single user input – complex
parameter output in combination with sophisticated hierarchical sound signature management enables
the designer to harness the vast field of variants and keep up consistence. Precision editing for fullaccess to the sound synthesis is possible in an expert editor below the Meta-Editor GUI. High-level
editors can be created employing 1D, 2D, 3D models to accommodate ever increasing complexity.
5.3 Hierarchically managing sub-brand and segmentation sound signatures
1D Gravity Maps may be used to manage an OEM’s sub-brands. Dragging the cursor horizontallyinterpolates the sound signatures loaded in the background to create new intermediate tunings. These
can then be defined as the design references for the next hierarchic layer below, the vehicle segments.

Figure 5 – 1D sub-brands Gravity Map
1D Gravity Maps may also be used to manage an OEM’s vehicle segmentation. Dragging the cursor
horizontally interpolates the sound signatures loaded in the background to create new intermediatetunings. These can then be defined as the design references for the next hierarchic layer below, thedriver-selectable sound signatures.

Figure 6 – 1D vehicle segments Gravity Map
5.4 Hierarchically managing vehicles’ sound signatures
Below a 2D Gravity Map for sub-brand 1 in the C Segment for creating the driver-selectable soundsignatures is shown. Weighted interpolation between 4 poles takes place, each representing a reference
signed-off sound signature.

Optionally, additional permeating sonic attributes, like e.g. sportiness, a take-off effect or other sonictrait, can be inserted. In the figures below it is displayed as a blue curvy pattern in the background.

Figure 7 – 2D Gravity Map for a sub-brand 1 / C segment vehicle
Below a 2D Gravity Map for sub-brand 2, C Segment for creating the driver-selectable soundsignatures is shown. Weighted interpolation between 4 poles takes place.

Figure 8 – 2D Gravity Map for a sub-brand 2 / C segment vehicle

5.5 Keeping related vehicles consistent across sub-brands and segments
Hierarchical authoring systems like Gravity Maps serve as a powerful vehicle for sound designers tocreate and maintain sonic coherence across sub-brands, segments, models, variants, and driveraccessible sound signatures within an OEM’s audio brand architecture by centrally governing all
signed-off sound signatures and deriving new sonic variants from them employing consistentparameter interpolation and processing methods.

Figure 9 – Cross sub-brand consistency management for related vehicles: sports-car example

6. CONCLUSION
Active Sound Design has been established as a standard feature of today’s state-of-the-art electricvehicles. Apart from the legislative requirement to create an acoustic pedestrian warning (AVAS) an
increasing number of vehicles equipped with interior and exterior sound generation has recently been
introduced to the market.
In the early years of the new medium ASD in EV, OEMs have with very few exceptions opted toimitate the sound of EVs’ technological predecessors, be the references found in sonic reality, engine
behavior, or science-fiction movies. Following the first phase, in the age of maturing EV sound design
manufacturers will increasingly look beyond imitating and make the EV become a medium in its own
right. They will take advantage of its yet to be fully discovered potential to create new sonic qualities
and insights from its abundant capabilities. Real-time sonification of Hyperobjects for the driver’s
information and embedding seems the most plausible and promising candidate at this time.
Expanding sub-brand, segment, and vehicle model palettes with ever increasing variations bring about
exponentially growing complexity for the contemporary sound designer. To maintain audio brandcoherence across all an OEM’s sub-brands, segments, models, and variants, an authoring system
deploying graphical meta-editors and a method-based backbone for creation, structured derivation,and management of audio CI-based sound signature content has been suggested.

REFERENCES
1. Bodden M., Belschner T., Comprehensive Active Sound Design. Part 1: electric and combustion vehicles.
Proceedings of the Internoise 2014. Melbourne, Australia 2014.
2. Marshall McLuhan, Understanding Media. The Extensions of Man. McGraw-Hill, New York 1964.
3. Friedrich Kittler, Optical Media. Merve, Berlin 2002.
4. Bentele G., Bystrina I., Semiotik. Kohlhammer, Stuttgart 1978; Tugendhat E., Wolf U., Logischsemantische Propädeutik. Reclam, Stuttgart 1982.
5. https://www.youtube.com/watch?v=1GVDE79rXLE (viewed 2022-08-15).
6. https://www.youtube.com/watch?v=FnLrbxcuvVg (viewed 2022-08-15).
7. Timothy Morton, Hyperobjects. University of Minnesota Press, Minnesota 2013.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022 in Gyeongju, Korea

ABS-0826

Indian Customer NVH Preferences
Pruthviraj THANGARAJ1
1

Hyundai Motor India Engineering, India

ABSTRACTThis study examines Indian customer’s perception regarding NVH performance of Hyundai Motor India
models based on the results from a nationwide survey. The survey’s output will serve as an important input
for future product development & sporty muffler direction for Indian market. As of now, the survey has been
conducted twice (2017 and 2020). The first one-to-one survey took place in 2017 in major Indian cities. The
second online survey took place throughout the country. Questions related to current NVH performance of
their car, expectations and preferences. NVH for future cars of same segment categories in survey are idle,
drive, road and wind noise. Customer’s typically face much traffic while driving in cities in India. In suchconditions, they can hear and depending on their driving style, they can feel different engine noise. Amongroad and wind noise attributes, overall road noise ranks first, then low frequency road noise and then windnoise. Similarly, customers liked the Sporty Muffler note, post launch of a car A, as did media. We
recommend isolated drive/idle NVH, as well as sporty note, for Indian market.
Keywords: Idle, Drive, Preferences

1. INTRODUCTION
Four Wheelers are becoming a major mode of transport nowadays. So, many of the automobile
manufacturers are focusing to catchup the market by their unique design, features, comfort factors,
etc. When speaking about the comfort factor of passenger cars, NVH is one of the major comfort
factor. Nowadays many customers and media personnel have started commenting about NVH of a carin internet. To maintain the brand name in market every OEM are focusing on their products and NVH
plays a vital role in that. Indian customers have started to notice noise and vibration in static anddynamic conditions.
Because of the different road conditions, climatic condition, the NVH perception and requirement
differs from person to person. Some customer’s will focus on noise; some will focus on vibration.
Some customers prefer their car to be very silent. To know and understand the Indian market NVH
requirement and customer’s preferences a survey was conducted throughout PAN India.
The survey was classified and collected the opinion on below categories- Current Model NVH Performance Acceptance- Future Car NVH requirement- N Line Priority in India

2. Current Model NVH Performance Acceptance
2.1 Classification
Majorly it was classified into Idle, Drive & Road/Wind. Each category was classified as follow:
Idle – Engine noise, Booming, Vibration & Blower noise
Drive – Engine noise, Booming, Vibration and High Gear Low RPM drive booming
Road Noise – Low and High Frequency road noise
And finally followed by Wind noise.
In this segment, the survey was carried out to know the performance of their current car and as
well as their expectation in their current car.

1

Pruthviraj.t@hmie.co.in2.2 Current Performance
Among the survey respondents, overall, their current car performs well in terms of noise, vibration,and harshness based on the results. Compact and sedan customers are satisfied across the board withthe current performance, which doesn’t mean that the SUV customers are not satisfied. But relatively
lesser than compact & sedan customer’s
2.3 Current Car Expectation
Coming to the point of customer expectation in their current car, they expect Idle & Drive NVH to
be first then followed by Road and Wind noise. In the case of SUV segment, customers are happy with
their Idle and Drive NVH performance and marginally satisfied with Road and Wind noise
performance.
If we see the expectation of SUV customers, they are as Idle NVH, Drive NVH, Road and Wind
noise performance should be equal.
The overall survey opinion of current model NVH performance acceptance vs. expectation wasgraphically represented in the figure 1

Figure 1 – Performance vs. Expectation (Present Model)
To summarize the overall current car performance vs. expectation on segment w ise
1. Compact & SUV: O/All Idle & Drive NVH performance better than expectation.
Road noise performance just meets expectations, followed by inferiorWind noise
2. Sedan: O/All Idle & Drive NVH performance is better than expectation.
Wind noise performance just meets the expectation, followed by road noise

3. FUTURE CAR NVH REQUIREMENT
Customer’s requirement of their future car also examined as segment wise and categories based.
The ranking was based on the number of responses by customer’s as most to least preferred.
This segment was mainly classified into three categories as below
1. IDLE NVH
2. DRIVE NVH
3. ROAD/WIND Noise3.1 Idle NVH
Idle NVH is an important factor while considering the typical India traffic conditions. India isbound to Signal system in all the cities. Most of the time customers are intended to face traffic inmany places in the cities. At that time, customers are meant to be in idle for some time, almost 60sec,
in some places it will be 75sec, 90sec, 120sec also. In those cases, they can feel the idle noise, booming
and vibrations clearly.
In India customers can face Stop & Go traffic condition in severely congested traffic places. In thiscase exposure to Idle noise & vibration more, as shown in figure 2

Figure 2 – Indian Traffic Conditions
This scenario was explained to the survey respondents and also based on their opinion, the analysis
results of future car requirement are as follow, Engine noise is most important factor than Vibration,
Booming and Blower noise. This trend is similar in Compact, Sedan and SUV segments. It was
illustrated in figure 3

Figure 3 – Idle Future Car Requirement Ranking
3.2 Drive NVH
Most of the customers will use their cars for city driving only, highway or long drive or very less.
In the city driving condition they have to drive like in low speed or low RPM. In this case they canable to observe many noise and booming. Not only from their car, it may be from external sources
also. Because in India wherever we go all the places will be heavily crowded by both people as wellas two wheelers and four wheelers. And India is very well known for the horn usage in all places. So,
it’s obviously important factor, that anyone can get easily annoyed by the noise. By considering this,many customers want their car to be very silent.

Figure 4 – City & Highway Driving Conditions

Based on the respondent’s the analyzed results are, Compact & Sedan have the same trend as follow.
Engine noise is more important than Booming, Vibration and High Gear Low RPM Booming. In caseof SUV segment customers, the trend is little different than others like, Engine noise, Vibration,
Booming & High Gear low RPM booming.
However, in this segment vibration and booming are almost similar ranking. And as per the
respondents and the real road conditions as explained above engine noise is most important than all
in all compact, sedan and SUV segments as illustrated in figure 5

Figure 5 – Drive Future Car Requirement Ranking
3.3 Road & Wind Noise
India is one of the country with many tourist places throughout the country in all the directions.
Most of the cities in India have nearby weekend getaway or gathering places for their leisure.
To visit the tourist places there are many transport ways like Train, Bus and Airways. But most of
the people will prefer to travel by car for their tourist plan because they can enjoy the road ways.
Indian Road statistics and major tourist places are mentioned in the figure 6

Figure 6 – India Major Tourist & Road Statistics
Some of the enthusiastic customers and young generation peoples are interested to drive in highspeed at highways and some people will prefer to go for a long drive sometimes by car. And theconstant speed limit most of the customers will drive is 60kph and 80kph. And for taxi 80kph is the
speed limit as per government rule and regulation. These are the cases where road noise can be audible
for the passengers.

Figure 7 – Road Conditions in India
In India we can find different kinds of road surfaces, like asphalt, smooth asphalt, coarse, gravel,
concrete, sand and rock mixed roads, etc. So road noise become one of the important factor andnowadays many customers are having some knowledge about road noise. Different types of roadconditions available in India are represented in figure 7 for example
By considering the above road conditions and based on the respondent’s opinion, the analyzed
results are, the trend is similar for all the segments as follow, overall road noise is more importantfactor than Low frequency and high frequency road noise followed by wind noise.
Generally, most of the customers are known about the road noise. But they cannot able todifferentiate between road booming, rumble, cavity. But their point is they can feel the road noise
when they are driving in diff kinds of surfaces, also while taking U -turn in mid or high speed. This issome customers concern we came to know that by this survey.
To summarize the future car requirement of customers, an illustration was made in figure 8

Figure 8 – Road/Wind Future Car Requirements Ranking

O/All the requirements across all the segments and the categories (Idle, Drive, Road & Wind)
remains the same.
- Idle NVH: Noise & Vibration both are equally in top priority. Blower noise doesn’t bothercustomers much. Due to congested traffic, stop and Go conditions, idle noise and vibration
is expected to be good.
- Drive NVH: Engine noise penetration during drive is considered as the high priority.
Booming is in 2 nd priority. Because in India, both in city and Highway constant throttle
input driving is not common. Due to less regularized traffic (vehicles not following fast andslow lane). So, acceleration and deceleration engine noise penetration to be less expected.
- Road & Wind Noise: O/All road noise is considered to be high priority and followed bywind noise as least priority.

4. N-LINE PRIORITY IN INDIA
Indian market is slowly growing up in sport cars and Sporty note mufflers experiences. Most of
the customers are not familiar with sporty note, because till some years ago it was mostly available inhigh end premium brand cars only in India.
Considering the normal customers also, some manufacturers in India just fine tuned their engine
and mufflers to showcase their cars as sportier in market. Partially we can agree those cars are bit
different from normal cars. But they cannot be considered as complete sporty car or a proper sportynote.
In this scenario, Hyundai planned and launched a N Line car in India. Thus, Hyundai attained abrand image for launching N Line series first time in India, which tuned well with a sporty notemuffler and design.
After the successful launch in compact segment, many customers were liked the sporty note
performance. Because many normal customers feel the proper sporty note for the first time in cities,
since it is launched within the affordable price. It attracts the people by its sporty note on road. Many
media personnel were appreciated about the N-Line & Sporty note characteristics.
The major appreciated comments about the compact segment N -Line car by Hyundai was
mentioned in the figure 9

Figure 9 – Media Opinion about Sporty Muffler

5. CONCLUSIONS
NVH is the ultimate comfort oriented factor, in which customers can feel the premium ness of the
car and it will create a good brand image among the customers. The one factor, not only by the driver
but also by all the passengers can feel easily is NVH.
As per the above study outcome and based on the ranking by customer’s, it is clear that the Engine
noise is the most commonly preferred factor irrespective of segments and categories. This is becauseof the typical traffic situation in India. Most of the time, customer will face too much traffic, so they
start noticing the engine noise & Idle NVH performance. Also, while driving in Indian cities, eachand every person have their own way of driving style. Because of that noise will vary and it will
penetrate their ears.,This clearly gives us the clarification and direction to the Indian car manufacturers to focus mainly
on engine noise irrespective of segments. It doesn’t mean that booming and vibrations are notimportant.
Vibration is the 2 nd important factor in idle condition for compact, sedan and SUV segments.
Booming and vibration is almost equally ranked in drive condition for compact, sedan and SUV
segments
By this survey, we came to know that, nowadays most of the Indian people are well educ ated andthey are well versed to use and surf internet. By this facility they surf more and understand about thecars based on their requirement. Also, customers and media have started commenting about NVH ininternet. So, most of the customers are influenced by the comments and expecting a very silent car,there should be no noise & vibration. Practically that is not possible, but still they are insisting andexpecting this silent factor also with better sporty note.

ACKNOWLEDGEMENTS
Based on my study I understood the NVH requirement of my country in detail and will continue
to survey on particular interval of time to understand more and keep updated the current scenario s ofmarket. I suggest the other manufacturers also to initiate survey like this in their countries tounderstand their needs and will be helpful for them in developing their vehicles.

REFERENCES
1. Basic Road Statistics https://morth.nic.in/sites/default/files/Basic%20_Road_Statics_of_India.pdf
2016~2017
2. Cartoon vector map of India. Travel illustration with indian main cities, by asantosg

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022, in Gyeongju, Korea
Extended abstractABS-0848

A Study on Electric Car Sound Design
M. Ercan Altinsoy1
1

Chair of Acoustics and Haptics, Technische Universitaet Dresden, Germany

ABSTRACTInterior car sound is a result of the emitted sounds by several sound sources. The existence and dominanceof the sound sources at electric cars differ in comparison to gasoline cars. The electric motor itself, thefrequency inverter, the gear mechanism or the battery charger can generate audible high frequency tonalsounds. Nowadays, most of the OEMs try to define specification sheets for the individual suppliers.
Depending to our experience with gasoline cars, these tonal components should be eliminated or masked.
However, the expectation of the customers for the electric car sounds can differ from the expectations for thegasoline car sounds. In our recent investigation, we observed that some expectation differences between thecustomer groups. Particularly young and new car drivers are much more tolerant for new and tonal sounds
rather than other customer groups. The aims of this investigation are to evaluate the perception of interior
electric car sound and give some guidelines for the definition of specification sheets and active sound design(ASD) applications. A listening test was conducted to evaluate the sound quality of recorded, modified anddesigned sounds. The tonality, the harmony and the degree of consonance of the tonal components were taken
into account.
Keywords: Vehicle acoustics, active sound design, tonality, sound quality

1. INTRODUCTION
Cars are emotional products (1,2). As we expect from similar type of products, if we drive a car,we would like to have some good and pleasant emotions. Therefore, the emotional design gains more
and more importance in the vehicle industry. Besides of the visual design, sound and haptic design
are very important in the case of vehicle design. In various car magazines or online platforms, it ispossible to read several sentences, such as “enjoy the melody of driving with…”, “sound machine”,“intimate sweet engine sound” or “engine sound is used to give drivers an ego boost”, whichcharacterize the sound based emotions of the drivers (1,2).
Drivers have long term experiences with internal combustion engine cars. These experiences
influence strongly on our expectations to the electric car sounds. Our recent investigations show that
there are some expectation differences between the drivers who have long term or short term
experiences with cars. Big amount of new drivers is much more tolerant to tonal components ofelectric car sounds. However, big amount of long term drivers is critical to tonal components ofelectric car sounds and don’t like them. Electric and internal combustion engine cars have somesimilar sound sources, such as wind noise and road-tire noise. However, electric cars have different
sound sources such as electric engine, inverter, converter which generate high frequency tonalcomponents. The relationship between the tonal components and their prominence can play animportant role on the perception of the car sounds. The car manufacturers try to develop guidelines
and specification sheets for the tonal components.
In this study, the pleasantness, which caused by interior noise of various electric cars, wasevaluated in listening tests. The focus was on the tonal components and their role on the annoyance
perception.

2. STIMULI, LISTENING TEST AND RESULTS
The interior noise of three electric cars, which are available on the market, were recorded using abinaural headset. The driving condition was the acceleration from 0 to 50 km/h. Then additional sound1

ercan.altinsoy@tu-dresden.destimuli were generated by adding or filtering important frequency components (e.g. tonal components
using band pass filters, high or low frequency ranges using high or low pass filters).
Twelve subjects, six men and six women aged between twenty-nine and fifty-seven years,
participated in the experiment. The subjects had no specific knowledge regarding acoustics or vibrations. All of the subjects were paid for their participation on an hourly basis. The binaurally
recorded sounds and modifications were presented to the participants through HEAD acoustics HA
II.1 headphones using a PEQ IV equalizer for an aurally-accurate reproduction. The experiments wereconducted in a sound-attenuating room.
Stimuli were presented in random order. The subjects were asked to evaluate the pleasantness ofthe sounds on a quasi-continuous scale, for which Rohrmann had tested the equidistance ofneighboring categories (Figure 1) (Rohrmann, 1978 ()). The length of the slider was 100 mm with aresolution of 1 mm. The score on this scale was equal to the distance (mm) from the left end of thebar. A graphical user interface in MATLAB was implemented for the evaluation experiments. In the
training phase, all of the participants were presented with different combinations of stimuli fromacross the full stimulus range, and they were then familiarized with the procedure of the experiment.

not at allslightly

moderately

very

extremely

Figure 1. A schematic representation of the evaluation scale.
The annoyance judgments for the electric car interior sounds were averaged, and the mean scoresand standard deviations are shown in Figure 2. The statistical analysis showed that there are no
specific subject groups, who have different preferences in this experiment. One reason for that is
possibly the limited number of participants at the experiment and the age group. The youngest
participant at the experiment is 29 years old.

Figure 2. Pleasantness ratings of the electric car interior sounds.

The analysis of the stimuli shows that the tonality plays an important role on the pleasantness
perception which is in line with previous studies. However, the interaction between the tonality andloudness is very important. If the interior noise is loud, the loudness perception dominates thepleasantness ratings and the role of the tonality will be weaker. The relationship between the tonalcomponents generate different perceptions as in music. For example, major chords evoke mostly
positive emotions, and minor chords evoke mostly negative emotions. Some trends were observed,
but it was not possible to have an exact parameterization.

3. CONCLUSION
In this study, the pleasantness interior sound of electric cars was evaluated. The results show thatthe perceived pleasantness of the various interior noises can vary strongly. The loudness and thetonality are most important parameters for the pleasantness perception. The number of participants
and sounds were limited in this study. It is planned to increase the number of participants and stimuli
in the following experiments. In this study, it was not possible to observe various preference groups.
The younger participants will be also taken into account to analyze if there are various subject groups
with different pleasantness preferences.

REFERENCES
1. Altinsoy, M. E. Challenges in the Vehicle Acoustics - What We Can Learn from Music? INTER-NOISE
and NOISE-CON Congress and Conference Proceedings, InterNoise17, Hong Kong CHINA, Institute of
Noise Control Engineering pp. 3031-3033(3), 2017.
2. Altinsoy, M. E., Jekosch, U. The semantic space of vehicle sounds: Developing a semantic differential
with regard to customer perception. Journal of the Audio Engineering Society, 2012; 60(1/2), 13-20.
3. Lennström, D., Nykänen, A. Interior sound of today's electric cars: tonal content, levels and frequency
distribution (No. 2015-01-2367). SAE Technical Paper, 2015.
4. Bodden, M., & Belschner, T. Comprehensive automotive active sound design-Part 1: electric andcombustion vehicles. In INTER-NOISE and NOISE-CON congress and conference proceedings (Vol. 249,
No. 4, pp. 3214-3219). Institute of Noise Control Engineering, 2014.
5. Lennstrm, D., Gutierrez Melian, A., Nyknen, A., Becker, J., Sottek, R. Perception of tones below 1 kHzin electric vehicles. In INTER-NOISE and NOISE-CON Congress and Conference Proceedings (Vol. 259,
No. 6, pp. 3675-3685). Institute of Noise Control Engineering, 2019.
6. Schecker, D., Deisling, E., Uerlings, P. Estimation of interior sound quality in BEVs regarding
measurements on the NVH-Chassis-Dynamometer: focusing on tonal components (No. 2020-01-1542).
SAE Technical Paper, 2020.
7. Mosquera-Sanchez, J. A., Sarrazin, M., Janssens, K., De Oliveira, L. P. R., Desmet, W. Multiple target
sound quality balance for hybrid electric powertrain noise. Mechanical Systems and Signal Processing,2018; 99, 478-503.
8. Parizet, E., Ellermeier, W., Robart, R. Auditory warnings for electric vehicles: Detectability in normalvision and visually-impaired listeners. Applied Acoustics, 2014; 86, 50-58.
9. Doleschal, F., Rottengruber, H., Verhey, J. L. Influence parameters on the perceived magnitude of tonal
content of electric vehicle interior sounds. Applied Acoustics, 2021; 181, 108155.
10. Doleschal, F., Verhey, J. L. Pleasantness and magnitude of tonal content of electric vehicle interior sounds
containing subharmonics. Applied Acoustics, 185, 2022; 108442.
11. Steinbach, L., Altinsoy, M. E. Prediction of annoyance evaluations of electric vehicle noise by using
artificial neural networks. Applied Acoustics, 2019; 145, 149-158.
12. Steinbach, L., Altinsoy, E., & Rosenkranz, R. Evaluation of Multiple Electric Vehicle Sources and New
Concepts Regarding Speed-Dependency. In INTER-NOISE and NOISE-CON Congress and Conference
Proceedings (Vol. 254, No. 2, pp. 78-85). Institute of Noise Control Engineering, 2017.
13. Altinsoy, M. E. The Evaluation of Conventional, Electric and Hybrid Electric Passenger Car Pass-By
Noise Annoyance Using Psychoacoustical Properties. Applied Sciences, 2022; 12(10), 5146.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022, in Gyeongju, Korea
Extended abstractABS-0206

Study of surface interaction through AVN contact joint case
Sangjun Yeo1; Hyeontae Shin1; Sunguk Choi2; Minyoung Shon1, †1Industrial Engineer Chemistry, Pukyong National University, Korea
2

Body test team 3, Hyundai Motor, Korea

ABSTRACT
Polymer materials in electronic devices and car industries are gradually increasing to satisfy lightweight andexcellent anti-corrosive performance demands. However, the stick-slip noise caused by the frictional
instability motion between polymer materials appeared. This study revealed the stick-slip noise andmechanism of plastic parts in a car's panoramic curved display (PCD) by observing the relative displacementof plastic parts (PC-ABS/PC-ABS and PC-ABS/STS 430) using a close-up camera and an acceleration sensor.
Based on the observation of stick-slip noise in PCD, a pad-on-disk frictional test was conducted to verify themain parameter of stick-slip noise. In the pad-on-disk frictional test, four polymer materials (PC-ABS, ABS,
PP, PTFE) with different surface energy were used, and the test conditions were set with various verticalloads and sliding speeds. As a result, PTFE (the lowest surface energy; 21.37 mN/m) showed the lowest
friction force, indicating the lowest possibility of stick-slip noise occurrence. On the other hand, ABS (thehighest surface energy; 44.21 mN/m) had the highest friction force and frictional instability, indicating thehighest possibility of stick-slip noise occurrence. Consequently, it is revealed that the possibility of stick-slip
noise generation is highly related to the surface energy of polymer materials.
Keywords: Polymer, Stick-Slip, BSR

1. INTRODUCTION
The demand for lightweight and excellent anti-corrosion performance is increasing in industries
such as electronic devices and automobiles. To satisfy this, the use of high-performance plastics called
engineering plastics to replace mechanical parts and exteriors that were traditional metal materials is
gradually increasing. However, relative motion occurs due to the difference in relative displacement
according to the temperature between these polymer materials, and stick -slip noise appears due to thisunstable frictional motion. Recently, as research has been actively conducted to popularize electricvehicles and reduce electronic device self-generated noise, uncontrollable stick-slip noise acts as anabnormal noise for consumers, and thus many complaints are being raised. Furthermore, in summer,
the temperature inside the car rises due to external heat, and the thermally expanded parts of the carcontract rapidly due to the operation of the indoor air conditioner, thereby generating a relative
displacement between the car parts and generating stick-slip noise.
In 1985, A. Josel et al. investigated the adhesion force of polyisobutylene and various polymers, andit was proposed that adhesive energy was correlated to the polymer's surface energy. (1)In 2017, J.S.Park et al. suggested that the stick-slip amplitude is proportional to the polar componentof surface energy components of polymer materials and that friction instability could be reduced by
reducing the polar component. (2)In this study, we suggested experimental method and equipment for friction characteristics according
to the surface energy between four polymer materials with different surface energy: ABS(Acrylonitrile-Butadiene-Styrene), PC-ABS (Polycarbonate-Acrylonitrile-Butadiene-Styrene), PP(Polypropylene), PTFE (Polytetrafluoroethylene) After that, through experiments, the friction
behavior that varies depending on the vertical load and sliding speed of the polymer material was
1

oploveqo2@naver.com†

myshon@pknu.ac.kr

analyzed to be closely related to the possibility of generating stick-slip noise and the surface energyof the polymer material.

2. EXPERIMENT
2.1 Materials
PC-ABS and STS 430 was supplied by Hyundai motor Co., Ltd. ABS and PP was purchased fromSunkyungplatech company (South Korea). PTFE was purchased from Alphaflon company (South
Korea).
2.2 Experiments
The relative displacement of plastic parts (PC-ABS/PC-ABS and PC-ABS/STS 430) was measured
with a close-up camera and an acceleration sensor to identify the mechanisms of the plastic parts andthe stick-slip noise from the panoramic curved display (PCD) of the car. Furthermore, to determine
the main parameters of stick-slip noise based on stick-slip noise observed in PCD, we observed the
stick-slip behavior of four different polymers of ABS, PC-ABS, PP, and PTFE, and STS 430
combination ABS and PC-ABS via a pad-on-disk type tribometer.
The contact angle was measured to obtain the surface energy of the polymer by configuring t he
device, as shown in Fig. 1. Two reference liquids (distilled water and diiodomethane) were used. Then,the surface energy of each material was calculated by the Owens–Wendt–Rabel–Kaelble (OWRK)
method (3, 4) based on the contact angle between the two reference liquids, and the sum of the twocomponents composed of polar and dispersion is considered the surface energy(5) as listed in Table 1.
Pad-on-disk type tribometer (J&L Tech) was used to observe frictional behavior between polymer
materials, as shown in Fig. 2.
All reference polymer specimens (specimen-B in Fig. 2) used in the tribometer friction test wereflat plates of 50x50x3 mm. The comparative specimens (specimen-A in Fig. 2) were also flat plates
having the size of 10x10x3 mm. In order to prevent the surface roughness of the specimen from
affecting the adhesion of the polymer specimen and the subsequent stick-slip behavior, the surface ofthe specimen is polished with sandpaper (No.2000), washed several times with ethanol, and stored ina desiccator before use in the experiment. After polishing, roughness parameters were measured using
Confocal Laser Scanning Microscope (LSM-700, Zeiss) analysis. The stick-slip test was conductedunder the vertical load condition in the range of 2N-50N and the sliding speed in the range of 0.150mm/min.

Fig. 1. Equipment set-up of contact angel tester
Table. 1. Surface energies of water and diiodomethane with polar and dispersion component.
Surface Energy

Polar Component

Dispersion Component

γp + γd (mN/m)

γp (mN/m)

γd (mN/m)

Water

72.8

51.0

21.8

Diiodomethane

50.8

0

50.8

OWRK method

(a)
(b)
Fig. 2. (a) Equipment set-up of pad on disk tribometer tester and (b) schematic diagram

3. RESULTS AND DISCUSSION
Stick-slip mechanism
In the close-up camera, plastic parts with contact in PCD were heat treated at 70°C for 15 min,and the relative displacement between plastic contact parts, including PC-ABS/PC-ABS (Fig.3) and
PC-ABS/STS 430 (Fig.4) was observed depending on the temperature change. Consequently, stick slip noise was observed through acceleration sensors (Fig. 3(b) and 4(b)), and visual observation andthe mechanism of stick-slip noise generating was frictional instability due to the expansion andcontraction of contact area by the heat in PCD. Weak stick-slip noise was generated in PC-ABS/PCABS with low contact force (Fig. 3(a)); on the other hand, strong stick-slip noise occurred in PCABS/STS 430 with high contact force.
3.1

Fig. 3. Close-up camera images of PCD plastic parts (PC-ABS/PC-ABS combination) by observing therelative displacement. Before heat treatment (a1) and after heat treatment (a2).
Acceleration sensor data of PCD plastic parts (PC-ABS/PC-ABS combination) (b)Fig. 4. Close-up camera images of PCD plastic parts (PC-ABS/STS 430 combination) by observing therelative displacement. Before heat treatment (a1) and after heat treatment (a2).
Acceleration sensor data of PCD plastic parts (PC-ABS/STS 430 combination) (b).
3.2

Surface properties of materials

According to A. Zosel et al., the material's surface energy plays an important role in friction andwear of the material and is related to the static frictional force (1). Furthermore, J.S.Park et al.
reported that the polar component of the surface energy was correlated with the stick -slip noise(2).
From the previous studies, the material's surface properties, especially surface energy, affectadhesion at the sliding interface, resulting in a stick-slip noise. Therefore, in this study, surface
energy was measured for four polymer materials (ABS, PC-ABS, PP, PTFE) with different surface
energy and STS 430 to observe the relationship between the material's surface energy and stick -slip
noise.
The surface energy was calculated using the contact angle values of the two reference liquids(water and diiodomethane) using Owens-Wendt-Rabel-Kaelble (OWRK) method as shown in Fig. 5
and Table 2. In Table 2, ABS has the highest surface energy and polar component with a surface
energy of 44.21 mN/m and a polar component of 2.14 mN/m, and PTFE has the lowest surface
energy of 21.37 mN/m and the polar component of 0.74 mN/m. The STS 430, a metal material, has avery high polarity component of 6.60mN/m but relatively low surface energy of 33.49mN/m.
Through this experiment, high static friction is expected in th e friction test in the order of ABS>PCABS>STS 430>PP>PTFE, which is the order of surface energy, and high stick -slip motion is
expected in the order of STS 430>ABS>PC-ABS>PP>PTFE.
7
6
40

5
4

30

Polar

Surface energy (mN/m)

50

3
2
20
1
10

0

ABS PC-ABS

PPPTFE STS 430

Fig. 5. The results of surface energy and polar component.

Table. 2. Surface energy, polar and dispersion components of materials calculated
based on the WORK method using two reference liquids (water and diiodomethane).
Surface Energy

MaterialspPolar Component

Dispersion Componentdpγ + γ (mN/m)γ (mN/m)

γd (mN/m)

ABS

44.21

2.14

42.07

PC-ABS

44.06

2.05

42.01

PP

30.83

0.81

30.02

PTFE

21.37

0.74

20.63

STS 430

33.49

6.60

26.89

Since the surface roughness of each specimen may affect the adhesion at the sliding interface (6),the surfaces of all specimens were polished with sandpaper (No. 2,000). After that, the averagesurface roughness (Ra) and the root mean square surface roughness (R q) of all specimens were
measured using a Confocal Laser Scanning Microscope, and the results are shown in Table 3.
From the results, the surface roughness parameters of each specimen showed substantially similarlevels, indicating that the surface roughness had li ttle effect on the observation of stick-slip noise in
the frictional test of this study.
Table. 3. Surface roughness parameters of materials after polishingusing a confocal laser scanning microscope.
Surface of Specimen

Ra (µm)Rq (µm)

ABS

0.30

0.40

PC-ABS

0.35

0.45

PP

0.35

0.46

PTFE

0.34

0.43

STS 430

0.41

0.45

3.3 Friction properties of materials
The friction test confirmed that the coefficient of kinetic friction decreased as the load increased(2 N to 50 N). Also, as the sliding speed increased (0.1 mm/min to 50 mm/min), the static friction
coefficient decreased, so it was judged that stick-slip noise would not occur at high sliding speeds.
The friction coefficient values and stick-slip noise are shown in Table. 4 and 5, and Fig. 6 and 7. As
a result, PTFE with the lowest surface energy (21.37 mN/m) showed the lowest friction and friction
instability, while ABS material with the highest surface energy (44.21 mN/m) showed the highest
friction and friction instability. (fig. 8) Consequently, the possibility of stick-slip noise is closely
related to the surface energy of the polymer material.

Table. 4. Kinetic friction force for each material according to
the vertical load at the sliding speed of 0.5mm/min. (ABS, PC-ABS, PP, PTFE)
Kinetic Friction (N)N

ABS

PC-ABS

PP

PTFE

2

0.309 ± 0.3

0.2651 ± 0.2

0.01370.104

50.28340.25430.1122

0.074

7

0.31

0.22950.11330.0732100.29240.21170.11590.067

200.21340.21350.14030.043

300.19210.22000.12030.036

400.18870.20570.10880.034

500.17670.1812

0.09150.034

ABSPC-ABS
PP
PTFE
0

10

20

30

40

50

Load (N)Fig. 6. Generation of stick-slip motion through friction test according to vertical loads.
(x; no stick-slip, o; occurrence of stick-slip)
Table. 5. Maximum static friction force for each material according tosliding speed at the vertical load of 10N. (ABS, PC-ABS, PP, PTFE)
Maximum Static Friction (N)mm/minABS

PC-ABS

PP

PTFE

0.1

0.52930.17600.13810.10190.5

0.44890.23410.11600.069

10.39030.30700.10810.068

100.27740.26770.16300.068

200.23430.19740.18520.060

300.18620.20620.19760.053

400.14110.22580.22130.053

50

0.10500.20040.23040.0500ABS
PC-ABS
PP
PTFE
0

10

20

30

40

50

Sliding speed (mm/min)Fig. 7. Generation of stick-slip motion through friction test according to sliding speeds.
(x; no stick-slip, o; occurrence of stick-slip)

0.5

ABS
ABS/STS 430
PC-ABS/STS 430
PCABS
PP
PTFE

Load (N)

0.4

0.3

0.2

0.1

0.0
0

5

10

15

20

25

30

Time (s)Fig. 8. Friction test according to the effect of the combination of materials.

ACKNOWLEDGEMENTS
The authors greatly appreciate the financial and technical support on this work by Hyundai Motor
Company.

REFERENCES
1. Zosel A. Adhesion and tack of polymers: Influence of mechanical properties and surface tensions. Colloid
and Polymer Science. 1985;263(7):541-53.
2. Park JS, Lee SM, Joo BS, Jang H. The effect of material properties on the stick–slip behavior of polymers:
A case study with PMMA, PC, PTFE, and PVC. Wear. 2017;378-379:11-6.
3. Owens DK, Wendt RC. Estimation of the surface free energy of polymers. Journal of Applied Polymer
Science. 1969;13(8):1741-7.
4. Kaelble DH, Uy KC. A Reinterpretation of Organic Liquid-Polytetrafluoroethylene Surface Interactions.
The Journal of Adhesion. 1970;2(1):50-60.
5. Wu S. Polar and Nonpolar Interactions in Adhesion. The Journal of Adhesion. 1973;5(1):39-55.
6. Myshkin N K PMI. Mechanical behavior of plastics: surface properties and tribology. Totten G, Liang H:
New York: Marcel Dekker; 2004. 57-94 p.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022 in Gyeongju, Korea

ABS-0506

In-line BSR quality inspection by using a deep learningtechnology
Jaesun Lee1, Youngkey Kim2*, Sunghyo Park1 and Sung-uk Choi4
1

Smart Factory Team, SM Instruments Co., Ltd., Korea
2CEO, SM Instruments Co., Ltd., Korea

4

Body Test Team 3, HYUNDAI MOTOR GROUP, Korea

ABSTRACT
Automobile parts are subjected to BSR quality inspection at the last stage of production lines. Traditionallya human operator hears BSR and evaluates it with a bear ears to judge pass or fail. There are strong needs
towards automating the inspection process in the production lines to perform quality inspection objectively.
In the automated inspection lines, data is measured and analyzed using sensors such as microphones andaccelerometers. Traditionally, various signal processing methods are used in order to find indexes that canevaluates the characteristics of faulty products. However, it is not easy to find the inspection indexes, and it
requires a lot of time and effort. To solve this problem, we propose a method that uses a deep learningtechnology. Applying the deep learning method to the BSR quality inspection can reduce the time and effort
of finding the inspection indexes. If sufficient data and proper deep learning methods are used, the inspection
can be implemented in a relatively short time.
Keywords: BSR, Deep Learning, Quality Inspection, Noise and Vibration

1. INTRODUCTION
Automobile parts that generate noise and vibration on their own are subjected to BSR quality
inspection at the last stage of the production line. Traditionally a human hears BSR and evaluates it
to make a pass or fail decision. If it is difficult to hear the noise due to background noise, it is also
possible to measure the noise indirectly by vibration sensors. The inspection is a very important stepto prevent the delivery of low-quality products to the customer. In the inspection process, averagecharacteristic of the products are important to make the criterion for judgment. If the product to beinspected generates noise above average, it is treated as a defective product. Although the level of thenoise helps in the judgment, most products are judged by complex criteria such as sound quality andtonality and so on. In a general production line with stable condition, a small amount within 1% isjudged as defective. In order to perform the inspection objectively and stably, automation is an
essential step.
As the performance of signal processing methods and measuring instruments increase, the noiseand vibration quality inspection process has been rapidly automated. In automated inspection lines,
data is measured using a microphone or accelerometer. Until now, the characteristics of the failure are
extracted from the data by traditional signal processing methods. Based on the signal processing result,a feature index that can analyze the characteristics of a faulty product has been calculated. Inspection
tests has been performed through the statistical characteristics of this index. In the case of generalnoise, band power is generally used as an index. When complex failures of bearings are involved,
advanced signal processing methods such as envelope and cepstrum are included. When these
automated methods are successfully applied, objective and reliable inspections can be performed.
However, it is not easy to find the inspection index in real world. It requires a lot of time and effort.

Although the industry provides tools to easily apply advanced signal processing and take various
indices, it is still a process that requires advanced manpower and takes a long time.
In this paper, a deep learning method is applied to the noise and vibration quality inspection. It
reduce the process and effort to find the inspection index. In the process of automating inspection
using conventional signal processing, it takes months to years to stabilize inspection indexes. As we
can see from various deep learning examples, if sufficient data and deep learning methods are used,
we can complete our tack in a relatively short time.[1][2] However, the use of deep learning
presupposes that a lot of data must be secured in advance. Since it is very difficult to secure various
data in advance in the actual field, a method to solve these difficulties is also proposed in this paper.

2. Application of CNN through image conversion of noise and vibration data
2.1 Deep Learning Application Review
The first step of deep learning of noise and vibration quality inspection is the confirmation of
inspection feasibility. Deep learning has already been used for visional quality inspection, but
examples of noise and vibration quality inspection are not readily available. In this study, as a firststep, noise and vibration data was changed to image data through spectrogram technology (Fig. 1). As
a next step the well-known CNN (Convolutional Neural Network) was applied to evaluates thepossibility of inspection.[3][4] As shown in Figure 1, the change in frequency and magnitude overtime can be observed at a glance through the spectrogram. If these characteristics are learned through
CNN, we can expect good test results.

Figure 1 – Example of image conversion of vibration signal through spectrogram
2.2 CNN Application Results
For the experimental application of CNN, the inspection results of the inspection line using theexisting signal processing method were used. The total measurement length of the data is 7.3 seconds,
and the sample is 16 kHz. When converting to an image simply using a spectrogram, the size of theimage is very large, so the image is filtered and then resized. A CNN with three convolutional layers
was used, and very basic techniques such as dropout and batch normalization were applied. The
training was conducted using 500 data of good and defective products, respectively, and it was verifiedthrough 500 defective products. The training and test results are shown in Figure 2. It converges very
well with a total accuracy of 99.2%. The accuracy of the original label is not 100%, and consideringthat a very basic algorithm is applied, it is a very high number.

Figure 2 –Total Accuracy Displaying Convergence Results for Good and Defect Judgment Using CNN

2.3 Problems to be solved for production site application
The most important problem to be solved in order to utilize noise and vibration quality inspectionin the field is securing data like other deep learning problems. In general, only normal products areproduced at the production site, and defective products are produced very rarely. As can be seen in
Table 1, it takes 60 days to secure 60 defective samples from a production line with a defect rate of
0.1%. On the other hand, the total number of units produced during the same period reached 106,000.
Therefore, it is generally impossible to secure a balanced number of data required when deep learning
is applied. In addition, it is difficult to easily perform a labeling operation on the secured defective
samples. Due to price and size issues, defective samples cannot be stored in the factory. It is possible
to measure and store various data, but since the types of defects are also very diverse, it requires a lotof effort to systematically perform this. The same is true for auto parts above a certain level. In thedata collection stage, the type and quantity of defects cannot be identified, so it is difficult to securedata unless all defective samples are stored. Therefore, it can be seen that in order to apply deeplearning to noise and vibration quality inspection, it is essential to apply the deep learning method
using the unlabeled data group composed mostly of normal products.
Table 1 – Defective units and total production units according to defect rate

3. Anomaly Detection
3.1 Anomaly Detection concept
In order to develop a deep learning method that selects defective products using data containing
mostly normal products, the method focused on in this study is the Anomaly Detection method.[5][6]Unlike the classification method, the anomaly inspection method learns the characteristics of normal
data and detects defects through it. As conceptually shown in Figure 3, using methods such as
Generative Adversarial Networks (GAN) or Variational Auto Encoder (VAE) are used to sufficiently
learn data features and find outliers. It's like finding an arrow moving away from the target. It is amethod of finding the center and radius of the arrow from the location of the arrow of hundreds ofshots previously hit, and identifying the striking point far away from it as an a bnormal hit. In thisstudy, the anomaly detection method was implemented using DCGAN (Deep Convolutional
Generative Adversarial Networks), and for convenience, the name was called the Doberman Anomaly
Detection method.
The advantage that can be obtained by using the Doberman abnormality test developed in this study
for the noise and vibration quality inspection of the production line is very large. First, education ispossible without information or data on defective products. Since training is conducted us ing only
good products, it is useful for noise quality inspection of production lines where it is difficult to obtain
defective products. Therefore, it is possible to start the inspection immediately even without specificinformation about the defect. Second, index values according to the test results are assigned to allmeasurement data. The degree of deviation from the median value of a good product is displayed as
an anomaly score. Accordingly, it is possible to grasp the quality level of the produced pr oduct in realtime. Third, as a result, it dramatically reduces the time for stabilizing the noise and vibration quality
inspection. Inspection can be started without defective products, and objective and consistent data canbe generated in a short time.

Figure 3 – Conceptual Description of Anomaly Detection Method Using GAN or VAE
3.2 Verification of inspection performance using MNIST data
The performance of the quality inspection through the Doberman anomaly inspection developed inthis study can be confirmed through the image data of the MNIST (Modified National Institute ofStandard and Technology) database. MINIST data is data in which numbers from ‘0’ to ‘9’ are written
by hand, and all data is labeled. This data is mainly used for the performance test of the classification
method. In order to confirm the performance of the anomaly test, an approach can be used in which
one out of a total of 10 data is considered normal and the rest are considered abnormal. That is, if we
assume that '0' is normal data, we can train the deep learning network using only '0' and check whether
it is well judged as 'Anomaly' when data other than '0' is received.
In this study, a deep learning network was trained using 2000 '0's, and 1000 '0's and '1000 non zero' numbers were input and the results were confirmed. Figure 4 shows the result through ahistogram. It can be seen that most of the Anomaly Scores are very low when '0' is input, and highabnormal scores are output when a number other than '0' is input. In addition, it can be seen that when
‘9’ or ‘6’ is used interchangeably with ‘0’, an abnormal score in the middle is output. This is apredictable result and shows that the Doberman anomaly test method trained using only '0's works
great.

Figure 4 – Validation of Doberman Anomaly Detection Method Using MNIST Database

3.3 Anomaly Detection Software Development
The application of the Doberman anomaly detection method developed in this study can be divided
into three steps and consists of commercial software. The first step is to convert noise or vibration
data into an image. Since preprocessing is required according to the size and resolution ofnoise/vibration data, it can be configured in one step. For convenience, it was called the Doberman
Explorer. The second step is the process of training the Doberman network through the data converted
into images. In order to check the accuracy of education, it is necessary to check the statisticalcharacteristics. According to the purpose of the function, it was called the Doberman Tra iner. The
third stage is the software that performs the actual inspection using the trained network. It is designed
to be as simple as possible with an emphasis on fast application of the trained network. It was called
the Doberman Detective. Using the above three steps, the abnormality detection method developed inthis study can be applied systematically and quickly. In Figure 5 below, you can see the software
configuration divided into three stages.

Figure 5 – The Dobermann Anomaly Detection method configured through three steps

4. Example of production site application
The Doberman anomaly inspection method developed in this study is applied in various ways tothe noise and vibration inspection of actual products such as automobile parts. In th is article, wewould like to introduce the inspection cases of motors and bearings used in automobile parts.
Frequency analysis, envelope analysis, and cepstrum analysis are generally required for noise
inspection of motors using existing signal processing methods. In order to determine the failure of the
bearing included in the shaft of the motor, the diagnosis of the failure frequency is performed throughthe envelope. The failure frequency is calculated using the number of balls included in the bearing
and the diameters of inner and outer rings, and whether an impulse occurs according to this frequency
is found through envelope analysis and cepstrum analysis. While it has the advantage of accurately

locating the cause of the failure, it requires a relatively complex stabilization process such as tuning
the bandpass filter and checking the level difference with the background noise in the process offinding the envelope.
If the Dobermann Anomaly Detection method is used, the complex signal processing process canbe omitted immediately. If you have enough quality data, you can easily train the network through theDoberman Explorer and trainer. Figure 6 shows the results of noise and vibration quality inspectionof parts composed of motors/gears/bearings that go into the steering system among automobile parts.
The horizontal axis represents the production time, and the vertical axis represents the Anomaly Score.
Each circle represents each part measured. If a boundary line is drawn near the ano maly score of 0.1
considering the normal distribution, abnormal samples can be easily identified.

Figure 6 – Application example of Doberman anomaly inspection of steering system parts
Figure 7 shows the results of the noise and vibration quality inspection of the actuator that goes
into the air conditioner among automobile parts. The vertical axis represents the state of the product,
and the horizontal axis represents the Anomaly Score. Each circle represents each actuator measured.
Training was performed based on the manufactured product, and verification was performed in thesecond half with the master product (normal product, defective product). As shown in Figure 7, if aboundary line is drawn near the abnormal score of 1.5 considering the normal d istribution, it can be
confirmed that all defective master products can be detected.

Figure 7 – Application example of Doberman anomaly inspection of actuator5. CONCLUSIONSIn this study, a deep learning method for BSR noise quality inspection of automobile parts
production lines was introduced. A deep learning method was proposed to point out the stabilization
time problem of the existing method using signal processing and to solve it. In order to solve theproblems in the field, it was pointed out that the problem of asymmetric data should be solved, andAnomaly Detection was suggested as a solution. We constructed our own method using DCGAN(Deep Convolutional Generative Adversarial Network) and called it Doberman. The applicability andaccuracy of the Doberman were confirmed by applying the data of MNIST. It was confirmed that it
can be used in the actual field through the application case of the electric appliance motor. As
mentioned at the beginning of this paper, the deep learning method drama tically shortens the time tocalculate the inspection index. In addition, the reliability of the developed inspection index does notvary depending on the developer, and always has a constant reliability. Therefore, when applied to a
production line, it is possible to save time due to the development of the inspection process and, as aresult, bring about an innovative cost check.

Figure 8 – Expected effect when applying BSR quality inspection of deep learning method

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https://en.wikipedia.org/wiki/Convolutional_neural_network, 2019. 12. 30
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