# Influence of temporal aspects of aircraft sound signature on perceived unpleasantness

Arthur Paté, Catherine Lavandier, Antoine Minard

► To cite this version:

Arthur Paté, Catherine Lavandier, Antoine Minard. Influence of temporal aspects of aircraft sound signature on perceived unpleasantness. Internoise 2015, Aug 2015, San Francisco, United States. hal-03379117

HAL Id: hal-03379117

https://hal.science/hal-03379117

Submitted on 14 Oct 2021

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_2.png" /></figure>

9-12 AUGUST 2015

**inter.noise 2015**

SAN FRANCISCO, CALIFORNIA USA

# INFLUENCE OF TEMPORAL ASPECTS OF AIRCRAFT SOUND SIGNATURE ON PERCEIVED UNPLEASANTNESS

Arthur Paté<fnref n="1" /> and Catherine Lavandier

Université de Cergy-Pontoise, laboratoire MRTE
5 mail Gay-Lussac, Neuville-sur-Oise
95031 Cergy-Pontoise Cedex, France

Antoine Minard

Genesis
Domaine du Petit Arbois, BP 69,
13545 Aix-en-Provence Cedex 4, France

This paper focuses on the temporal aspects of aircraft flyover noise signals and their psychological effects. Stimuli are equalized according to EPNL: this avoids possible loudness masking effects. Features describing the temporal evolution of the sound level are studied: rising slope, duration and amount of fluctuations due to turbulences. A synthesis-by-recording method supplies realistic signals, which are further processed in order to design a set of stimuli uncoupling temporal features one from another. This gives e.g. short signals with smooth slope, or long signals with sharp slope. The stimuli are rated by subjects on semantic scales handling the following sound aspects: "long / short", "sudden / gradual", "slow / fast", "steady / staccato-like", and "not unpleasant at all / extremely unpleasant". The output of the psychoacoustic test is statistically analyzed. At constant EPNL, the stimuli are perceptually organized along two dimensions. First the "dura-tion" aspect is correlated with the length of the signal part having a sound level above PNLmax-10PNdB, and with the PNLmax. Second the "staccato-like" aspect (correspond-ing to the amount of fluctuations) is correlated with the "fluctuation strength". Moreover, it is shown that this "staccato-like" aspect contributes to the overall unpleasantness of the aircraft flyover noise.

## 1 INTRODUCTION

Reducing the annoyance due to aircraft noise has been a major concern for several decades. Non-acoustical factors [1–3] explain a large part of the declared annoyance variance, but about a third of the annoyance can be explained by the exposure to sound [4]. A high overall sound level seems to be the first reason of annoyance so a lot of studies have focused on the relationship between sound level and annoyance [5, 6].

<footnote n="1">¹email: arthur.pate@u-cergy.fr</footnote>
Other parameters such as the number of aircraft [7], the take-off and landing trajectories [8], have also been investigated in order to better characterize the sound exposure.

Some studies [3, 9, 10] suggested that improving the sound quality of aircraft sound signatures may reduce the annoyance experienced by citizens living in the neighborhood of airports. This study is in line with that approach: it investigates the effects of temporal aspects of aircraft flyover sound signatures on unpleasantness.

**1.1 Overview of the PARASOFT project**

This work is part of a joint project called PARASOFT (PsychoAcoustics Research aiming at Assessing SOnic unpleasantness or aircraFT flyovers), funded by the FRAE (Fondation de recherche pour l'aéronautique et l'espace / Research foundation for aeronautics and space) [11], bringing together researchers from the university of Cergy-Pontoise (UCP) [12], Genesis [13], INSA Lyon [14], and ONERA [15].

The aim of the project is to characterize the acoustic signature of aircraft flyovers, with respect to the unpleasantness experienced by people living near airports. Spectral aspects are investigated by the team at INSA Lyon [16]. Our team at UCP focuses on the temporal aspects of the aircraft flyover noise.

**1.2 Temporal aspects of aircraft sound signatures**

Previous studies (for aircraft [10] or train [17] pass-by noise, or even for automotive traffic noise [18]) identified some temporal aspects which appeared to be meaningful to characterize the unpleasantness experienced by listeners.

**The slope** is computed from the time evolution of the noise level. This slope has been calculated from the sound level in dB(A) for trains, but for aircraft it has been decided to work with the PNL [19–21]. The slope calculation requires the localization of the times at which the PNL (integration time 0.5s, no tone-correction used) reaches its maximal value PNL$_{max}$, and at which it reaches the value PNL$_{max}$-10PNdB. The slope is straightforwardly derived from a linear regression of the PNL-curve between these two times. Fig. 1 illustrates the computation of the slope.

**The duration:** this is the time difference between the two instants where the level PNL$_{max}$-10PNdB is reached. Fig. 1 illustrates the computation of the duration.

**The amount of fluctuations:** it characterizes the amplitude modulation in the time-frequency representation of the signal due to turbulence effects. Fig. 2 shows the temporal evolution of the PNL for the 3 different amounts of fluctuations investigated here: low (F1), moderate (F2) and high (F3).

**2 SOUND STIMULI**

**2.1 Primary sounds**

The primary sounds are synthesized by partner Genesis. The synthesis method is described by Minard [22, 23]. Field recordings of airplane take-offs are analyzed and resynthesized with a simplified propagation model between the aircraft positions and the receiver point. This allows us to produce flyovers with different speeds and altitudes, hence with different slopes and durations (slope is likely to increase with low altitude and high speed, duration is likely to increase with high altitude and low speed). We thus have access to sounds varying over a great range in terms of slope and duration (Fig. 3).
<figure><img src="image_3.png" /><figcaption>Figure 1: Definition of the “slope“ and the “duration“. Top: temporal representation of an aircraft take-off noise. Bottom: PNL envelope of the same sound. PNL<sub>max</sub> is the maximum PNL, reached at t<sub>0</sub>. t<sub>1</sub> and t<sub>2</sub> are the times at which the level PNL<sub>max</sub>-10 PNdB is reached.</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>Figure 2: Temporal evolution of the PNL for the 3 different amounts of fluctuations (F1, F2 and F3) investigated in the study. Slope and duration are the same for the 3 signals.</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>Figure 3: Some aircraft noise signals represented on the slope vs. duration plane. Calculations are made on several airplanes with various speeds and altitudes, in order to identify a wide range of variation for slope and duration.</figcaption></figure>

## 2.2 Sound modifications

Slope and duration are found to be correlated (Fig. 3 shows a correlation coefficient of -0.81): a larger slope “sharpens” the PNL curve, thus reducing the duration. The aim of the present study is to create a corpus of stimuli within which the temporal features are uncorrelated, so that the own influence of each feature can be characterized. In this study, the envelopes of the signals are modified in order to obtain sounds with uncorrelated slope and duration. This changes the shape of the PNL curve over time: it is possible to alter the slope without modifying the duration, and vice versa.

Fig. 4 shows the pressure signal and the corresponding PNL curve of an “original” signal, i.e. as a direct output of the synthesis. The symmetry of the PNL curve can be altered in such a way that it changes the slope without changing the duration: this is shown in Fig. 5. The signals of Figs. 4 and 5 correspond to stimuli 24 and 25 respectively (see Fig. 6).

The amount of fluctuations is, by construction (amplitude modulations on the time-frequency representation of the signal), not correlated with the other temporal features. Three equally-spaced depths of amplitude modulation are investigated here (F1, F2 and F3).

In addition to these modifications, all sounds are equalized to 85 EPNdB (EPNL calculated without tone-correction) in order to minimize the masking effect of sound level. This is a typical level at the close vicinity of airport runways.

# 3 LISTENING TEST

## 3.1 Subjects

The subjects were recruited through a poster campaign at UCP. 34 people took part in the experiment. The average age of the subjects is 28 with a standard deviation of 13 (the youngest subject is 18 and the eldest is 62). 19 of them are students with no particular knowledge in acoustics, 11 are employees of the university, 2 are acousticians not involved in the current
<figure><img src="image_6.png" /><figcaption>Figure 4: Example of an “original“ signal (stimulus 24), i.e. as output by the synthesis, with original slope and duration. The temporal evolution of the pressure signal is in black and the temporal evolution of the PNL is in red.</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>Figure 5: Example of the modification of the signal of Fig. 4 (stimulus 23). The temporal evolution of the pressure signal is in black and the temporal evolution of the PNL is in red.</figcaption></figure>

project, and 2 come from outside the university. There are 18 females and 16 males. Audio-gram testing prior to the main test reported that all subjects have a normal audition (“normal or subnormal hearing” [24]).

## 3.2 Stimuli

The data set of the present test consists in 29 aircraft flyover noise signals, called the “stimuli”. They all have a length of 58 seconds, and they are all equalized to 85EPNdB. The stimuli are represented in the slope vs. duration plane in Fig. 6: some have been modified in order to cover the empty parts of the current slope vs. duration plane in Fig. 3. In order to describe the stimuli more easily, Fig. 6 also proposes a grid defining categories of duration (increasing from D1 to D4) and slope (increasing from S1 to S3).

## 3.3 Experimental setup

The tests took place in an acoustically dry room at the university (Fig. 7). The floor is fitted with artificial grass and the wall in front of the listening place is fitted with artificial green plants and a garden fence, in order to immerse the subjects into a cozy urban garden mood. A background noise is rendered by two loudspeakers hidden by the plants. The background noise consists in a 1.5 hour-long semi-urban neighborhood recording: it includes cars and trucks pass-bys and bird tweets. At the listening position, the equivalent sound level of the background noise is 49 dB(A), or 60 EPNdB. The flyover stimuli are perceived as emerging from the background noise since 2-second long fade in and fade out are applied to the stimuli.

The stimuli are presented through a calibrated RME Fireface UC soundcard feeding Senn-heiser HD650 headphones, onto which the sensor of a Polhemus Patriot 3D-tracker is mounted. The spatialization software is developed by partner Genesis: the binaural signal is corrected in real time according to the data from the head tracker, in order to fit the target trajectory. It makes the aircrafts have a straight front-to-rear trajectory, whatever the subject's head motion. The spatialization gives the opportunity to render more realistic aircraft flyover sounds. A schematic overview of the setup is shown in Fig. 8.

The test begins with a “training phase”: 3 stimuli are successively presented to the listener who is not expected to answer to any question at this point. Those 3 stimuli are always presented in the same order and are a priori believed to summarize the variation range of duration, slope
<figure><img src="image_8.png" /><figcaption>Figure 6: Representation of the stimuli (crosses) on the slope vs. duration plane. Ochre grid and brackets define categories of slope and duration. In addition to its number, each stimulus is characterized by its category of slope (S), duration (D), and amount of fluctuations (F, coded with blue, black and red for levels F1, F2 and F3 respectively).</figcaption></figure>

and amount of fluctuations, within the whole set of stimuli. The 3 sounds of the training phase
are the following:

➤ 7: short duration, sudden slope, medium amount of fluctuations,

➤ 12: long duration, sudden slope, medium amount of fluctuations,

➤ 19: long duration, gradual slope, high amount of fluctuations.

Then the listener is introduced to the test interface, which is written with *Matlab*. The 29 stimuli are randomly ordered. Each stimulus can be played back as many times as the listener wants. The listener can not stop the sound playback once started. The experimental method is a semantic differential procedure: for each stimulus the listener is asked to move a slider on 5 different continuous scales, each of those having 2 antonym adjectives as boundaries. Adjectives and scales are described in table 1. They have been proved to be meaningful to subjects [10]. The calculated “duration” is expected to describe well the perceived “short / long” aspect, and the calculated “slope” is expected to describe well the perceived “sudden / gradual” aspect. Table 2 summarizes the steps of the listening test.

# 4 RESULTS

## 4.1 Analysis method

A Jarque-Bera test for normality is done for each of the 145 (29 stimuli, 5 scales) distributions of 34 (number of subjects) evaluations. For 134 distributions, the test fails to reject the null hypothesis of normality (α=0.05). The 11 other cases still exhibit a unimodal distribution, so
<figure><img src="image_9.png" /><figcaption>Figure 7: Photograph of a subject doing the listening test in the laboratory.</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>Figure 8: Schematic overview of the experimental setup for the listening test.</figcaption></figure>

<table><caption>Table 1: Adjectives used as boundaries of the semantic scales: original French designation and English translation.</caption><thead><tr><th>Scale number</th><th>English translation<br>of the scale boundaries</th><th>French original designation<br>of the scale boundaries</th></tr></thead><tbody><tr><td>1</td><td>Steady vs. Staccato-like</td><td>Régulier vs. Saccadé</td></tr><tr><td>2</td><td>Slow vs. Fast</td><td>Lent vs. Rapide</td></tr><tr><td>3</td><td>Sudden vs. Gradual</td><td>Soudain vs. Progressif</td></tr><tr><td>4</td><td>Long vs. Short</td><td>Long vs. Court</td></tr><tr><td>5</td><td>Not unpleasant at all vs.<br>Extremely unpleasant</td><td>Pas du tout désagréable vs.<br>Extrêmement désagréable</td></tr></tbody></table>

the use of the mean value for each of the 145 distributions is meaningful.

A principal component analysis (PCA) is carried out on the mean evaluations of the 29 aircraft sound signals on the 5 scales. The software *Statistica* is used. Table 3 shows the eigenvalue, and the variance explained by each of the 5 factors of the PCA: the following of this paper will deal with the first 2 factors only, since they conjointly explain 86.99% of the variance (cumulative explained variance), and they have an eigenvalue greater than 1.

## 4.2 Axes of the PCA

Fig. 9 presents the projection of the adjectives (for each semantic scale, one of the boundaries is arbitrarily chosen to represent the whole scale) and the stimuli on the first 2 factors of the PCA.

The axes “long / short”, “slow / fast” and “sudden / gradual” are well correlated with the 1st axis of the PCA (C=-0.81**, C=-0.87** and C=0.89** respectively; where the superscript ** denotes a correlation coefficient significant at the level α=0.01 for 27 degrees of freedom). This indicates that the subjects produced similar answers on these 3 scales. The use of “short” and “fast” (correlation coefficient C=0.82**) to describe a similar sensation is not surprising: a high pass-by speed necessarily decreases the duration of the noise. On the contrary, the correlation between “gradual” and “short” (C=-0.57**) seems to reveal that the two physically uncorrelated dimensions referred to as “slope” and “duration” are not perceptually uncorrelated: a “short” flyover noise tends to be perceived as “sudden”.

The axis “steady / staccato-like” is well correlated with the 2nd axis of the PCA (C=-0.94**). It indicates that this aspect is perceived independently of the 1st axis, thus quite independently
<figure><img src="image_11.png" /><figcaption>Figure 9: Projection of the adjectives (blue) and stimuli (yellow crosses, and codes for slope, duration and amount of fluctuations categories) on the first 2 factors of the PCA. Green dashed (resp. red solid) arrows show the pairs of stimuli within which the duration (resp. slope) increases, all other parameters kept constant.</figcaption></figure>
<table><caption>Table 2. Summary of the steps of the listening test.</caption><thead><tr><th>Order</th><th>Task</th><th>Details</th><th>Avg. duration</th></tr></thead><tbody><tr><td>1</td><td>Audiogram</td><td>➤ Managed by the experimenter<br>➤ 10 sine tones at 20 dB (HL)</td><td>2 min.</td></tr><tr><td>2</td><td>Training phase</td><td>➤ 3 stimuli<br>➤ 29 stimuli presented in random order</td><td>8 min.</td></tr><tr><td>3</td><td>Test phase</td><td>➤ The first 2 stimuli are played back<br>again at the very end of the test phase,<br>answers are saved only at this point.</td><td>44 min.</td></tr><tr><td>4</td><td>Debriefing phase</td><td>➤ Gathering impressions from the listener</td><td>5 min.</td></tr></tbody></table>

<table><caption>Table 3. Eigenvalues and explained variance for the 5 factors of the PCA.</caption><thead><tr><th>Factor</th><th>Expl. variance (%)</th><th>Cum. expl. variance (%)</th><th>Eigenvalue</th></tr></thead><tbody><tr><td>1</td><td>51.51</td><td>51.51</td><td>2.58</td></tr><tr><td>2</td><td>35.48</td><td>86.99</td><td>1.77</td></tr><tr><td>3</td><td>5.72</td><td>92.71</td><td>0.29</td></tr><tr><td>4</td><td>4.28</td><td>97.00</td><td>0.21</td></tr><tr><td>5</td><td>3.00</td><td>100.00</td><td>0.15</td></tr></tbody></table>

of the aspects “short” (C=-0.24 with “staccato-like”), “fast” (C=-0.20) and “gradual” (C=-0.24).

Observations can be made from a closer examination of the projections of individual stimuli. First, all stimuli with a high amount of fluctuations (F3) are on the same side of the “staccato-like” axis, and well separated from the others. Stimuli with a low amount of fluctuations (F1) tend to lie on the other side of the “staccato-like” axis, but they remain close to the central stimuli with moderate amount of fluctuations (F2).

Second, stimuli with increasing duration (from D1 to D4) tend to be well sorted along the 1st axis of the PCA (as well as along the scales “long / short” and “slow / fast”).

Third, stimuli with increasing slope (from S1 to S3) are not sorted along the 1st axis of the PCA (and neither along the scale “sudden / gradual”).

The axis “not unpleasant at all / extremely unpleasant” is correlated with the 1st (C=-0.58**) and 2nd (C=-0.73**) axes of the PCA. The more “staccato-like” the flyover noise is, the more “unpleasant” (C=0.69**) it is: this result is in line with those of Barbot [10]. In contrast, neither of the “long / short” or “slow / fast” aspect is significantly correlated with the “unpleasantness” (C=0.15 and C=0.23 respectively), whereas the “sudden / gradual” aspect correlates with the “unpleasantness” (C=-0.54**).

## 4.3 Correlations with physical descriptors

This section attempts to describe each scale in terms of physical descriptors. The duration and slope are defined in Sec. 1.2, and the PNL is computed as recommended by the FAA [21], without tone-correction. Fluctuation strength is computed with the software LEA by partner Genesis, based on Sontacchi’s work [25]. Note that the speed is an input of the synthesis algorithm and was not computed from the output signal.

“Long / short” correlates with the speed (C=0.83**), and with the duration (C=-0.75**). In Fig. 9, stimuli differing only in the duration are linked together with a green dashed arrow.
Increasing the duration (following the green dashed arrows) seems to pull away stimuli from the “short” side: “calculated duration” (see Sec. 1.2) and “perceived duration” (answers from the subjects) match well. “Slow / fast” also correlates with the speed (C=0.79**), and with the duration (C=-0.73**). This gives a further indication that the “fast” and “short” aspects of flyover noise are linked together.

“Sudden / gradual” correlates with the speed (C=-0.64**) but not with the slope (C=-0.07). In Fig. 9, stimuli differing only in the slope are linked together with a red solid arrow. An increase in slope (along the red solid arrows) seems to produce in general a more “gradual” noise. This is in contradiction with the hypothesis that an increase of slope could be perceived as a more “sudden” flyover. The negative significant correlation between “gradual” and “fast” (C=-0.68**), and “gradual” and “short” (C=-0.57**) may indicate that there exists a link or a confusion between the perception of the “gradual” and “short” aspects of these modified stimuli. This could be explained by the natural negative correlation between the slope and the duration for real flyovers.

The fluctuation strength correlates with the scales “steady / staccato-like” (C=0.64**) and “not unpleasant at all / extremely unpleasant” (C=0.82**).

# 5 DISCUSSION

It can be noticed that the PNL<sub>max</sub> significantly correlates with the scales “slow / fast” (C=0.74**), “sudden / gradual” (C=-0.52**), “long / short” (C=0.69**) and “not unpleasant at all / extremely unpleasant” (C=0.47**). By construction, the PNL<sub>max</sub> also correlates with the computed duration (C=-0.94**) and speed (C=0.88**) for the following reason: increasing the signal duration is increasing the time during which the PNL takes high values; yet the equalization according to EPNL guarantees that all stimuli are perceived as equally loud over almost the whole flyover, the consequence is that signals with a higher duration necessarily have smaller PNL<sub>max</sub>. PNL<sub>max</sub> values range from 83.0 PNdB to 89.3 PNdB over the present corpus of stimuli. Such variations of peak levels are audible and may have been used by listeners in their evaluation of “unpleasantness” (together with the judgment of “staccato-like” and of “gradual”, the latter being presumably related to an evaluation of duration).

The influence of the peak level PNL<sub>max</sub> needs to be investigated in the future. The next step of the project is to re-introduce changes in sound level in order to compare the perceptive effects of temporal parameters to the perceptive effects of the sound level.

Further work will also focus on finding acoustic indicators that better fit the perceived “suddenness”, “duration”, and their interaction.

# 6 ACKNOWLEDGMENTS

This project is funded by the FNRAE through the project PARASOFT. The authors warmly thank all the subjects and beta-testers of the test.

# REFERENCES

[1] R. Guski. Personal and social variables as co-determinants of noise annoyance. *Noise & Health*, 1:45–56, 1999.

[2] K. Paunovic, B. Jakovljevic, and G. Belojevic. The importance of non-acoustical factors on noise annoyance of urban residents. In *9th international congress on noise as a public health problem (ICBEN)*, Foxwoods, CT, 2008.
[3] SEFA Report Summary, project number 502865 funded by the European Commission (FP6-AEROSPACE), 2007.

[4] M. Kroesen, E. J. E. Molin, and B. van Wee. Testing a theory of aircraft noise annoyance: A structural equation analysis. *Journal of the acoustical society of America*, 123:4250–4260, 2008.

[5] B. Berglund, U. Berglund, and T. Lindvall. Scaling loudness, noisiness, and annoyance of aircraft noise. *Journal of the acoustical society of America*, 57:930–934, 1974.

[6] R. Guski. Subjective and physical evaluation of aircraft noise. *Noise control engineering journal*, 35:61–63, 1990.

[7] J. Lambert, P. Champelovier, R. Blanchet, C. Lavandier, J. Terroir, F. Márki, B. Griefahn, U. Iemma, K. Janssens, and R. Bisping. Human response to simulated airport noise scenarios in home-like environments. *Applied acoustics*, 90:116–125, 2015.

[8] U. Iemma, L. Burghignoli, F. Centracchio, and V. Galluzzi. Multi-objective optimization of takeoff and landing procedures: level abatement vs. quality improvement of aircraft noise. In *Proceedings of Internoise 2014*, Melbourne, Australia, 2014.

[9] COSMA report summary, project number 234118 funded by the European Commission (FP7-TRANSPORT), 2013.

[10] B. Barbot, C. Lavandier, and P. Cheminée. Perceptual representation of aircraft sounds. *Applied Acoustics*, 69:1003–1016, 2008.

[11] Website of FRAE. http://www.fnrae.org/, 05/22/2015.

[12] Website of UCP. http://www.u-cergy.fr/fr/laboratoires/labo-mrte.html, 05/22/2015.

[13] Website of Genesis. http://genesis-acoustics.com/, 05/22/2015.

[14] Website of the laboratory for vibrations and acoustics (LVA). http://lva.insa-lyon.fr/, 05/22/2015.

[15] Website of ONERA. http://www.onera.fr/, 05/22/2015.

[16] L. Brocolini and E. Parizet. Influence of tonal components on the unpleasantness of airplane noise at take-off. In *Proceedings of Internoise 2015*, San Francisco, CA, 2015.

[17] J. Terroir and C. Lavandier. Perceptual impact of distance on high-speed train sound quality. *Acta Acustica united with Acustica*, 100:328–340, 2014.

[18] J. Morel, C. Marquis-Favre, D. Dubois, and M. Pierrette. Road traffic in urban areas: a perceptual and cognitive typology of pass-by noises. *Acta Acustica united with Acustica*, 98:166–178, 2012.

[19] K. D. Kryter. Scaling human reactions to the sound from aircraft. *Journal of the Acoustical Society of America*, 31:1415–1429, 1959.

[20] L. L. Beranek. The noisy dawn of the jet age. *Sound and vibration*, pages 94–99, january 2007.

[21] FAA. ICAO - Appendix 16 - vol. 1 - Environmental Protection - Aircraft Noise, 2008.
[22] A. Minard, G. Lemaitre, C. Lambourg, and P. Boussard. Synthèse de bruits de passage d'avion dans le cadre du projet PARASOFT. In *Proceedings of the Congrès français d'acoustique (French congress on acoustics)*, pages 1065-1071, Poitiers, France, 2014.

[23] A. Minard, S. Hourcade, C. Lambourg, and P. Boussard. Controllable sound simulations of aircraft flyovers. In *Proceedings of Internoise 2015*, San Francisco, CA, 2015.

[24] BIAP (International bureau for audiophonology). BIAP recommendation 02/1 bis - Audiometric classification of hearing impairments. http://www.biap.org, 1996.

[25] A. Sontacchi. Entwicklung eines Modulkonzeptes für die psychoakustische Geräuschanalyse unter Matlab. Master's thesis, TU Graz, Graz, Austria, 1998.