PROCEEDINGS of the23rd International Congress on Acoustics
9 to 13 September 2019 in Aachen, Germany

Microphone array method for determining noise angular energy
distribution on building envelopesMiodrag STANOJEVIĆ1; Miloš BJELIĆ; Dragana ŠUMARAC PAVLOVIĆ; Miomir MIJIĆ, TatjanaMILJKOVIĆ
School of Electrical Engineering, University of Belgrade, Serbia

ABSTRACTA method is proposed for determining traffic noise angular energy distribution on building envelopes using
the microphone array as a source localization tool. Measurement procedure is presented with the results
obtained by a nonuniform planar array developed for this purpose with microphone positions optimized for
traffic noise measurements. The results are obtained in one characteristic urban configuration – a non-canyonstreet. Results are commented and their implications on the in-situ sound reduction index of the façade
element are explained. The algorithms used for sound source localization are CB in the frequency domain,along with deconvolution algorithms DAMAS2 and CLEAN-SC, and their performance is compared. The
paper addresses the problem of the computational complexity of the algorithms which results in longexecution times and inhibits measurements on a larger scale. A method for algorithm optimization ispresented that includes the analysis of key characteristic events instead of an overall noise recording. Thesensitivity of output results on varying event recordings is analyzed. The goal is to create a time efficientmeasurement procedure which would enable the acquisition of a larger data set covering various urban
terrain configurations.
Keywords: microphone array, noise angular distribution, traffic noise

1. INTRODUCTION
The proposed method for noise analysis in urban areas is motivated by the fact that the part itions
used as façade elements are located in sound field conditions substantially different from laboratory
conditions, in which their sound insulation properties are measured and specified. As oppose to thediffuse sound field with uniform angle of incidence probability used in laboratory measurements, inreal scenarios these elements are located in a sound field where the probability of incidence is
non-uniform and contains prominent directions from which the majority of noise energy arrives.
ISO standard defines two measurement methods for measuring in-situ sound insulation of a façade:global loudspeaker method, and global traffic noise method (1). The loudspeaker measurement
involves a speaker positioned at an angle of 45±5° to the façade plane, in order to approximate traffic
noise incidence angles. It was reported (2) that the D2m,nT,w values obtained by these two types ofmeasurements differ by up to 8 dB. Among other factors, these differences are certainly influenced by
the varying angular distribution of noise energy in real situations.
Microphone arrays are a modern tool that enable the localization of the sound sources. The idea ofthe proposed method is to utilize a microphone array in order to gain an insight into angular
distribution of noise energy impeding building envelopes in urban environments. Initial research wasfocused on measurements and array signal processing algorithms in order to obtain a valid spatial
distribution of sound sources. A microphone array was designed specificall y for this application (3),and measurements were performed in urban environment (4,5). Certain array processing algorithms
have been used, and DAMAS2 algorithm (6) has been chosen as the main processing algorithm. The
goal of the research is to determine shapes of noise energy angular distributions specific to certain
characteristic urban configurations such as canyon and non-canyon streets. Based on thesedistributions, conclusions can be drawn on the expected performances of certain façade elements when
they are located in different urban configurations.
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miodragstanojevic@bitprojekt.co.rs

The practical realization of measurements and subsequent signal processing proved to be extremely
time consuming. This fact represents a problem which needs to be addressed in order to enablemeasurements on a larger scale. A large number of parameters which can be varied open a question ofoptimization of the measurement procedure. These parameters include spatial resolution ofbeamforming algorithm, time block length, the content of recorded signals, various algorithm specificvariables, etc.
This paper examines a method for optimizing the measurement procedure by reducing the recorded
signal time by recording and analyzing only specific events instead of a longer ambient recording. The
goal of the analysis presented in this paper is the recognition of key events happening in a certain urban
configuration and determining their characteristic shapes. Then, their individual influence on in-situ
sound reduction index is assessed, and their contribution to the overall angular distribution, which wasobtained for a longer overall signal recording.
Next section describes the measurement procedure and microphone array signal processing .
Results for angular distribution are given for a characteristic non-canyon street configuration insection three. The fourth chapter presents the results obtained by reducing the signal length to specific
events. The influence of this reduction on final results is discussed.

2. MEASUREMENT PROCEDURE
The measurements were performed using a microphone array specifically designed for this purpose.
It is a nonuniform planar microphone array with 24 omnidirectional microphones. The microphonearray characteristics were optimized for use in traffic noise analysis (7). Figure 1 shows themeasurement setup. Microphone array is placed parallel to the façade surface at the minimum possible
distance from the façade. Since planar microphone arrays possess spatial ambiguity, i.e. they havesymmetrical response in front and back plane, it is necessary to position the array as close to the façadeas possible, in order to mitigate source “smearing” which would appear in resulting images as aconsequence of façade reflections. Figure 1 on the right shows the street profile with relevant data for
calculations, the elevation angles of 110° and 137° correspond to the edges of the traffic lanes and areimportant in the interpretation of the resulting maps. For the measurements presented in this paper thecenter of the array is positioned at 3.2 m, and the distance to the middle of the street is 6 m. In thepresented scenario, there are no buildings opposite from the façade, hence this configuration belongs
to the category of non-canyon streets. The street is in a busy urban area with frequent car, bus andtrolleybus traffic.

Figure 1 – Measurement setup: microphone array on the façade (left), street profile (right).
The incidence angle and its correlation to azimuth and elevation angles are shown in Figure 2. The
figure on the left shows the incidence angle Θ defined in relation to the line perpendicular to the façadesurface. In order to obtain the angular distribution of the incident noise energy, the energycontributions for circular rings of width dΘ have to be calculated. The figure on the right shows anexample of the source distribution map obtained by beamforming algorithms. Since the spatial grid isdefined by two angles: azimuth and elevation, the resulting map is presente d as a two-dimensionalgraph with color coded values for sound source energy levels from different directions. The map
shown in Figure 2 is hypothetical and shows a uniform distribution of energy. The circles shown on
this map correspond to the dΘ rings on the left picture.

135Figure 2 – Incidence angle (left), the procedure for determining probability density function (right).
The algorithms used for array signal processing are: CB (Conventional Beamformer) (8) andDAMAS2 (Deconvolution Approach for the Mapping of Acoustic Sources) (6). The CB algorithm isused as a first step in space-time signal processing, and DAMAS2 is then applied to eliminate theinfluence of the microphone array from the resulting spatial map. Other deconvolution beamforming
algorithms were also examined, but DAMAS2 was chosen as the one giving the most accurate results(5). The results presented in the continuation also show the resulting map obtained by the CLEAN -SC
algorithm (9) for the purposes of comparison.

3. NOISE ENERGY ANGULAR DISTRIBUTION
Figure 3 shows the resulting maps obtained by the three mentioned algorithms. The results shownin the Figure correspond to the 1/3 octave band at 1250 Hz. The conclusions drawn here are valid forother frequency ranges as well. The range of azimuth angles is from -90° to +90°. And elevation angle
range is from 80° to 160°. The elevation angle range is reduced, since it was determined that there areno major sound sources present outside this region. All three figures contain black lines which signify
the edges and the middle of the traffic lane. The lines appear curved because it is a two -dimensional
representation of a half-sphere in front of the façade. The result obtained by CB algorithm is shown in
the top-left position. Observing this result would indicate a nearly uniform distribution of soundenergy, with only several directions containing sources of 10 dB higher power. The reason for this is
the nature of CB algorithm, namely the fact that the results are highly influenced by the m icrophonearray beampattern, which unavoidably contains certain sidelobes. This is why the application of thedeconvolution algorithms is necessary. However, CB algorithm is a necessary first step in every
deconvolution procedure. The top-right graph in Figure 3 shows the result obtained with CLEAN-SCalgorithm. This result indicates concentrations of the sound energy in the zones that correspond to thetraffic lanes, as expected. However, an approximately uniform distribution of the energy is observed inother directions, i.e. there is no increase in zones where the reflected sound is expected, such as parked
cars and pavement. Since the CLEAN-SC algorithm is based on the coherence between the main lobe
and the sidelobes, and by removing the coherent content, the algorithm will also remove thecontributions of reflections arriving with a relatively short time delay compared to the direct sound,incorrectly identifying them as sidelobe content. The bottom graph in Figure 3 shows the results ofDAMAS2 algorithm. This result also indicates the majority of the sound energy arriving from thedirection of the traffic lanes. However, in this result other regions can be observed as well, in which
there is an increase in sound energy level. These zones correspond to the location where reflective
surfaces exist, and their levels are approximately 10 dB lower than the main energy regions, which isan expected result.

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Figure 3 – Beamforming algorithm results. Conventional Beamformer (top-left), CLEAN-SC (top-right),
DAMAS2 (bottom)
Based on the conducted analyses, the results of DAMAS2 algorithms will be used to calculate theprobability density function of the noise energy angular distribution. However, CB and CLEAN -SC
are also important algorithms: CB is the necessary first step in all deconvolution techniques andCLEAN-SC algorithm can be used to determine the locations of the primary sources in a source spatial
distribution plot. For the purposes of comparison, the results obtained by energy summation o f theresults of all three algorithms are shown in Figure 4 for 1/3 octave band at 1250 Hz.

Figure 4 – Probabilty density functions for three different algorithms.

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The energy summation is performed as was explained in section 2 and shown in Figure 2. The pdfs
obtained by the CB and CLEAN-SC algorithms show a relatively uniform distribution for angles larger
than 20°, with the difference around the incidence angle of 40° where CLEAN-SC algorithm has aslight increase. This is the angle which corresponds to the traffic lanes. Both these results would
suggest a nearly uniform distribution of incidence energy for the angles between 20° and 90° degrees
which does not correspond to the real situation. The results obtained by summing the DAMAS2 source
distribution plot, show a highly non-uniform distribution with maximums around incidence angles of40° and 68°. These angles correspond to the highest energy contributions from the traffic lanes. It isinteresting to note that the highest energy arrives at the angle of 40° which approximately corresponds
to the standard incidence angle used in the global loudspeaker method for measuring façade soundinsulation.

Figure 5 – Sound reduction index based on DAMAS2 results and theoretical distribution sin(2θ). Concrete
20 cm (left), glass 5 mm (right).
Figure 5 shows the sound reduction index calculated based on the angular distribution obtained byDAMAS2 algorithm and based on a theoretical uniform distribution sin(2θ). The calculations areperformed for a concrete wall of 20 cm thickness and glass of 5 mm thickness. These two materials
were chosen, as they are some of the more commonly used façade elements. The angular distributions
were calculated based on DAMAS2 results for all frequency bands between 50 Hz and 5000 Hz. Basedon these distributions sound reduction index is calculated as a function of frequency. Differences canbe observed in the sound reduction index compared to the theoretical uniform distribution. Thesedifferences, for the concrete façade element are up to 12 dB for individual frequency bands. These
differences lead to a large difference in weighted sound reduction index, and also mean that this typeof façade will exhibit better sound insulation characteristics than theoretical calculation would suggest.
In the case of a glass partition, the values obtained by measured pdfs are generally higher thantheoretical values, except in the region of the frequency of coincidence for glass. This would suggestthat at these frequencies, the angular distribution of sound energy is substantially different from a
uniform sound field and favorizes larger incidence angles. It should be noted that these conclusions arevalid only for this particular measurement situation – a non-canyon street, with microphone arrayheight at 3.2 meters. In order to draw more general conclusions, a more extensive measurements indifferent configurations need to be performed.

4. MEASUREMENT OPTIMISATION
As was mentioned previously, signal processing stage is extremely time consuming. The
deconvolution algorithms are time consuming since they are based on an iterative procedure. In orderto calculate the probability density functions in all frequency bands for a 30-minute long recording,
100 hours are needed for computations on a standard PC. If there are any changes that need to be madein some of the parameters in order to perform comparative analysis, the computation time this longposes a serious problem. This is the reason, why steps are taken to optimize the measurement
procedure by reducing the computation time. One of the first steps in this analysis is finding means of
reducing the length of the signal that needs to be processed.
This analysis is based on the assumption that the overall probability density function observed at a30-minute time interval, is a combination of several key pdfs observed for characteristic events such as138

a car passing by, a trolleybus passing by, ambient hum observed with no characteristic events etc. If
this assumption is proven correct, it would mean that the measurement time and signal processing time
can be greatly reduced by recording only those key characteristic events and performing analyses only
on those recordings, given that all the types of events are known as well a nd their frequency ofoccurrence in a given situation.

Figure 6 – Probability density functions for different types of events. Cars (left), trolleybuses (middle), andambient sound (right).
Figure 6 shows the pdfs obtained for recordings of characteristic events excerpted from a 30 -minuterecording in the measurement scenario presented in Figure 1, in the 1/3 octave frequency range at1600 Hz. It can be seen that certain types of events produce similar pdfs. Both traffic events, cars andtrolley buses have pdfs with energy concentrated in the angular range from 40° to 60°. This
corresponds to the energy contributions from the directions of the traffic lanes. The pdfs obtained fromthe recordings of ambient sound, with no events, have maximums for larger angles of incidence. Thisis an important observation, knowing the fact that the sound reduction index values are lower for larger
angles of incidence.
In Figure 7 sound reduction index graphs are shown for two types of façade elements, and three
types of events. The top row represents the results for concrete wall of 20 cm thickness, and the bottom
row results for glass of 5 mm thickness. In all graphs black line represents the sound reduction values
calculated for the overall probability density functions obtained for a 30 -minute recording. The grayedarea represents the sound reduction index value range for each of the calculated event pdfs.

Figure 7 – Sound reduction index calculated based on different events pdf compared to a 30-minute recording.
Concrete 20 cm (top), glass 5 mm (bottom).
In the case of a concrete wall, for both types of traffic events, the sound reduction index curve
calculated for the overall recording is within the range of values spanned by different events, and the139

largest differences are in the frequency range from 500 Hz to 2000 Hz. However, in the case ofambient noise recording, the values of overall curve are outside the range of individual events. Similar
conclusion can be drawn for glass wall, with the largest differences observed in the case of ambient
noise recordings. The largest differences in the case of ambient noise recordings for glass elements,
between the overall curve and curves based on event pdfs are in the range from 2000 Hz to 2500 Hz,
which is around the coincidence frequency of 5 mm thick glass. As was demonstrated in Figure 6, thepdfs for ambient noise recordings have maximums at angles close to 90°, where sound reduction index
is reduced. This effect results in a large discrepancy in this case. It should be noted though that theenergy levels for these types of events are generally lower than for traffic types of events, which is thereason why these discrepancies have a lower influence on the overall sound reduction curve.

5. FINAL REMARKSThe paper proposes a measurement procedure for determining angular distribution of incident noise
energy in urban environments. The goal of such analysis is to determine specific shapes ofdistributions in characteristic urban configurations such as canyon, non -canyon streets, etc.
Microphone arrays are used as a tool for determining sound source spatial distribution. It was shownthat the choice of algorithm for array signal processing significantly influences the final result, andDAMAS2 algorithm was chosen as the most reliable. The results for predicted sound red uction index
based on measured energy distributions show certain differences compared to theoretical values. Inorder to draw more general conclusions, measurements need to be conducted on a larger scale: ondifferent locations with different terrain configurations. The measurement procedure and signal
processing need to be optimized in order to solve the problem of significant computation time neededto obtain the results. The idea is presented for reducing the processing time by recognizing key eventsand analyzing only their recordings. This procedure is based on the assumption that the overall angular
distribution is a combination of individual distributions characteristic for these events. It was shownthat the value of the sound reduction index depends on the type of sound source. In urban areas there
are different types of sources (cars, buses, trolleybuses, trams, etc.) which leads to a change in thestructure of the sound field. The consequence of this is the time dependence of the façade elementsound reduction index. The influence of a certain type of source is dependent on the concrete terrain
configuration in which the façade is located, the size of the source, the spectral content of its noise etc.
This is the reason why the same type of source, such as a car, can result in different distributions, andhence different sound reduction index values. This means that the simple summation of results basedon different types of events will not lead to the same result as the one based on overall recordin g. It istherefore necessary to determine certain correction factors which would take into account the nature ofdifferent sources and their influence on the general sound reduction index result. Introducing thesecorrection factors will be the focus of future research.

ACKNOWLEDGEMENTS
This paper is realized as a part of activities supported by Serbian Ministry of Science andEnvironmental Protection, project TR-36026.

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