Spurious noise in car cabinet can be not only annoying bust also indicative of some

potential faults. A small square microphone array with 4 sensors was adopted in this paper to

localize the sound source in car for fault diagnosis. A new voice activity detection (VAD) algorithm

was proposed for the typical discontinuous short-time noise in car due to some fault and applied to

direction of arrival (DOA) estimation as a pre-processing stage. Four different time delay estimation

methods were compared based on the measurements from a typical passenger. Experimental results

illustrate that the VAD algorithm is crucial to achieve robust fault localization performance and the

generalized cross-correlation method with phase transform weighting function is an appropriate

fault localizer in car

Ball, Andrew

Fazenda, Bruno

Fengshou, Gu

Guan, Luyang

Pengxiao, Teng

Yichun, Yang

English

University of Huddersfield Repository

University of Huddersfield RepositoryGuan, Luyang, Fengshou, Gu, Fazenda, Bruno, Ball, Andrew, Yichun, Yang and Pengxiao, TengDOA Estimation in Car for Abnormal Sound LocalisationOriginal CitationGuan, Luyang, Fengshou, Gu, Fazenda, Bruno, Ball, Andrew, Yichun, Yang and Pengxiao, Teng (2009) DOA Estimation in Car for Abnormal Sound Localisation. In: 8th International Conference on Damage Assessment of Structures, 3-5 August 2009, Beijing, China. (Submitted)This version is available at http://eprints.hud.ac.uk/3660/The University Repository is a digital collection of the research output of theUniversity, available on Open Access. Copyright and Moral Rights for the itemson this site are retained by the individual author and/or other copyright owners.Users may access full items free of charge; copies of full text items generallycan be reproduced, displayed or performed and given to third parties in anyformat or medium for personal research or study, educational or not-for-profitpurposes without prior permission or charge, provided:• The authors, title and full bibliographic details is credited in any copy;• A hyperlink and/or URL is included for the original metadata page; and• The content is not changed in any way.For more information, including our policy and submission procedure, pleasecontact the Repository Team at: E.mailbox@hud.ac.uk.http://eprints.hud.ac.uk/ DOA Estimation in Car for Abnormal Sound Localisation  Guan Luyang1,a, Gu Fengshou1,b, Bruno Fazenda1,c, Andrew Ball1,d,  Yang Yichun2,e, Teng Pengxiao2,f 1Diagnostic Engineering Research Group, University of Huddersfield, UK 2Institute of Acoustics, Chinese Academy of Sciences, P.R. China al.guan@hud.ac.uk,  bf.gu@hud.ac.uk,  cb.fazenda@hud.ac.uk,  da.ball@hud.ac.uk, eyychun@mail.ioa.ac.cn,  fpx.teng@mail.ioa.ac.cn Keywords: DOA estimation, VAD, fault localization  Abstract. Spurious noise in car cabinet can be not only annoying bust also indicative of some potential faults. A small square microphone array with 4 sensors was adopted in this paper to localize the sound source in car for fault diagnosis. A new voice activity detection (VAD) algorithm was proposed for the typical discontinuous short-time noise in car due to some fault and applied to direction of arrival (DOA) estimation as a pre-processing stage. Four different time delay estimation methods were compared based on the measurements from a typical passenger. Experimental results illustrate that the VAD algorithm is crucial to achieve robust fault localization performance and the generalized cross-correlation method with phase transform weighting function is an appropriate fault localizer in car. 1. Introduction Some special noise (rattles, squeaks, etc) in car cabinets are often the forewarning or representations of some faults. In fault diagnosis, one of the key issues is to localize the fault which emits the annoying noise. Microphone array diagnostics is an appropriate selection to search for such sound source. In this paper, a small microphone array with 4 sensors was applied to estimate the direction of the sound source in the car. Generalized cross-correlation (GCC) based time delay estimation methods were compared to select a good direction of arrival (DOA) estimator for an acoustically reflective environment such as a car cabinet [1, 2]. According to the characteristics of the target sound, a new VAD algorithm was proposed and applied to DOA estimation to obtain robust DOA performance for the non-stationary sound source. 2. ew VAD Method for DOA Estimation 2.1 Characteristics of squeak noise Fig. 1 shows a typical squeak noise in car. Obviously, it is discontinuous, not periodical and just of very short duration. Its spectrum shows that it’s wide-band sound, so the microphone array with small aperture can be adapted. However, signal content analysed during a very short time, for example about 0.1s, can be regarded as approximately stationary. In DOA estimation, if the discontinuous sound source was treated just as the stationary sound (white noise, etc), the performance will be interfered greatly during the silent pauses when signal-to-noise ratio (SNR) is very low. Therefore, in a similar fashion to speech recognition applications, VAD is indispensable to achieve better performance.    2.2 A new VAD algorithm for DOA estimation Here, a new VAD method was designed based on the short-time energy and the zero-crossing rate to extract the squeak noise events from the long-time time series. Assuming the input signal is ( )x i  1,2.....i = . This VAD algorithm can be described as follows: Step 1: Construct the two curves ( )s i  and ( )n i  which simulate the max energy level of the signal and the background noise respectively: if ( ) ( )        ( ) ( )else        ( ) ( 1) (1 ) ( )s ss i x is i x is i s i x iα α<== ⋅ − + − ⋅ (1) ( ) ( 1) (1 ) ( )n nn i n i x iα α= ⋅ − + − ⋅  (2) where sα  and nα are weighting parameters and both very close to 1. Step2: Calculate the short-time energy and zero-crossing rate of each data frame The new data ( )s i  and ( )n i  are divided into K  frames with the fixed length L  and overlapping. Then the average energy of each frame of data is: 21( ) ( )     1, 2...Ls kiE k s i L k K== =∑  (3) 21( ) ( )     1,2...Ln kiE k n i L k K== =∑  (4)  Zero-crossing rate is defined as [3]: ( ) ( )2( ) sgn ( ) sgn ( 1)LiZ k x i x i== − −∑  (5) where 1     ( 0)sgn( )1  ( 0)xxx≥= − < Step3: sE  is weighted by normalized zero-crossing rate ( ) ( ) ( ) max( )s sE k E k Z k Z′ = ⋅  (6) Step4: compare sE′  and nE  to make a decision  Fig 1  Time series and spectrum of squeak noise  Fig 2  VAD curves  if ( ) ( ),        decision: actives nE k E k′ >  (7) Fig. 2 shows the time domain signal and its VAD curves of the squeak noise. Obviously, the weighted short-time energy sE ′  is more appropriate to distinguish the two close events, for example, the two sound events occurred between 1.2-1.6s in Fig. 2. Based on the results of VAD algorithm, DOA estimator works only when the target noise occurs with high SNR. Therefore, not only the DOA estimation performance will be robust, but also the post-processing of the DOA estimation results will become easier and simpler. 3 GCC based DOA estimation In microphone array signal processing, time delay estimation (TDE) is one class of robust DOA estimation methods and has been applied to many research and practical fields successfully. A GCC based TDE method was adopted in this research.  GCC methods calculate the cross-correlation function xyR  based on the cross power spectrum density xyP  of the two signals x  and y : ( ) [ ( ) ( )] ( ) ( )xy xy xyR IFFT P W R wτ ω ω τ τ′= = ∗  (8) The key of GCC methods is the selection of an appropriate weighting function ( )W ω . In the work presented here, a white noise source was used to measure the performance of different weighting functions to select the appropriate DOA estimator for fault localization in the car. Four common weighting functions are described below:  3.1 The ROTH Processor (ROTH) The Roth weighting function uses one of the signals’ power spectrum density (PSD) as an approximation of the PSD of the original signal ( )s n  to make the cross-correlation function approach a pulse function. 1( )( )xxWPωω=  (9) 3.2 The Maximum-Likelihood Processor (ML) ML processor, also named HT processor, is defined as: 22( )1( )( ) 1 ( )xyxy xyWPρ ωωω ρ ω= ⋅− (10) where ( )xyρ ω is coherence function: ( )( )( ) ( )xyxyxx yyPP Pωρ ωω ω=  (11) where ( )xxP ω  and ( )yyP ω  are PSD of the two signals respectively.   3.3 The Smoothed Coherence Transform (SCOT) The definition of SCOT weighting function is as below: 1( )( ) ( )xx yyWP Pωω ω=  (12)  where ( )xxP ω  and ( )yyP ω  are PSD of the two input signals respectively. SCOT function is the most widely used weighting function.  3.4 The Phase Transform (PHAT) PHAT weighting function is defined as the reciprocal of cross power spectrum density. 1( )( )xyWPωω=  (13) For the input signals, it makes the weighted spectrum not sensitive to the source signal but the channel response. It performs more consistently when the characteristics of the source signal change over time and is more robust to reverberation than other cross-correlation based methods [1]. 4 Experimental Results 4.1 Experiment setup An experiment was carried out where the sound source was simulated by a speaker fixed at 8 different positions. At every position, the speaker played white noise and squeak noise respectively. The squeak noise is the real acoustic signal recorded in the car. A 4-sensor microphone array with a distance of about 0.18m between microphones was fixed in the center of the car cabinet. Fig. 3 shows the relative positions of array, sound source in the car and the definition of direction. θ  is the direction of sound source we expect to obtain.  In the experiment, the sampling rate was set at 44.1 kHz. The incoming signal was high-pass filtered and segmented into equal length frames of 0.128s and 50% overlapping. Source direction estimation is based on each data frame. Four GCC methods were adopted. According to the weighting functions described above, these methods are labelled as GCC_ROTH, GCC_ML, GCC_SCOT and GCC_PHAT. Since the sound source is fixed, the mean and standard deviation were calculated as the criterion for DOA estimation performance.  4.2 DOA estimation performance for white noise source Table 1 shows the DOA performance of the four methods for white noise source. It illustrates that GCC based TDE methods can localize the stationary sound source effectively in the car which is a reverberant environment. Among these four methods, GCC_PHAT achieved the best performance. This result is in consistent with the conclusion in [4].   Fig 3  Sketch of the experiment   4.3 DOA performance for squeak noise When the SNR is high, it is almost immune to the reflective environment in the car. However, if the sound is not continuous, such as the squeak noise shown in Fig. 1, the DOA estimation will be influenced by noise when the sound source pauses. Then the performance descends greatly.  Fig. 4 shows that when the sound source pauses, the DOA estimations are not reliable, and many results seem to be outliers. These outliers should be removed during post-processing; otherwise, the performance will be influenced badly.  (a)     (b)  (c)     (d) Fig 4  DOA estimation Performance of GCC_PHAT with or without VAD at different positions (a: front; b: left; c: rear-left; d: rear)  GCC ROTH GCC ML GCC SCOT GCC PHAT Right 359.1 (3.16) 358.0 (1.64) 358.2 (0.41) 358.1 (0.02) Front-right   69.7 (4.44)   70.3 (0.07)   70.3 (0.13)   70.3 (0.10) Front   91.9 (0.05)   92.1 (0.04)   91.9 (0.05)   92.0 (0.01) Front-left 122.2 (0.07) 122.1 (1.37) 122.3 (0.02) 122.3 (0.03) Left 182.8 (0.15) 182.9 (0.06) 182.9 (0.02) 182.9 (0.04) Rear-left 247.7 (0.05) 247.7 (0.04) 247.9 (0.01) 247.9 (0.01) Rear 275.7 (0.42) 275.3 (0.29) 276.9 (0.71) 276.6 (0.50) Rear-right 308.3 (1.74) 307.2 (0.05) 307.2 (0.03) 307.2 (0.03) Table 1  DOA performance for white noise source  If VAD is adopted before DOA estimation, the system estimates the sound source’s position only when the target sound occurs. With high SNR target sound signal, a more robust performance can be achieved even without the post-processing stage.   Table 2 is the mean and standard deviation of DOA estimation results of GCC_PHAT with and without VAD at different positions and the ‘desired direction’ column refers to the DOA result using a continuous white noise source.  These results illustrate that if no VAD is applied, the outliers will induce large standard deviation and degrade DOA performance obviously. When VAD is adopted, the smaller deviation and the more accurate mean value of DOA results represent a more robust performance. Therefore, VAD is crucial for discontinuous sound source localization. 5 Conclusions The typical noises due to some faults in car cabinet are commonly discontinuous in time domain and non-stationary in frequency domain. To localize the sound source accurately, a new VAD method was proposed as a pre-processing method and applied to DOA estimation for fault diagnosis. Experimental results show that GCC method with PHAT weighting function produces reliable and robust localization in the reverberant car cabinet and VAD is crucial to achieve robust DOA estimation performance. Considering a variety of acoustic interferences may exist in practical application, localization of sound sources with low SNR are now subject of future work by the authors.   References [1] J. Chen, J. Benesty and Y. Huang. Time Delay Estimation in Room Acoustic Environments: An overview. Journal on Applied Signal Processing. (2006), p. 1 [2] J. Chen, J. Benesty. Performance of GCC- and AMDF-Based Time-Delay Estimation in Practical Reverberant Environments. Journal on Applied Signal Proceesing. Vol. 1, (2006), p. 25 [3] J.R. Jang, Audio Signal Processing and Recognition, Information on http://www.cs.nthu.edu. tw/~jang [4] J.M. Peterson, K. Chris. Analysis of Source Localization in Reverberant Environments. IEEE Sensor Array and Multichannel Signal Processing. (2006), p. 672  Desired Direction GCC_PHAT without VAD GCC_PHAT with VAD Right 358.1 357.4  (23.78) 354.5  (5.76) Front-right   70.3   96.4  (51.46)   73.5  (7.83) Front   92.0   96.1  (23.66)   92.4  (0.71) Front-left 122.3 122.4  (49.20)) 122.5  (2.38) Left 182.9 180.8  (62.66) 182.1  (1.41) Rear-left 247.9 223.3  (49.59) 247.4  (0.38) Rear 276.6 230.5  (57.18) 274.7  (0.53) Rear-right 307.2 263.2  (18.00) 307.4  (1.93) Table 2  DOA performance for squeak noise source 

Analysis of Source Localization in Reverberant Environments.

Audio Signal Processing and Recognition, Information on http://www.cs.nthu.edu. tw/~jang

Performance of GCC- and AMDF-Based Time-Delay Estimation in Practical Reverberant Environments.

Time Delay Estimation in Room Acoustic Environments: An overview.

DOA Estimation in Car for Abnormal Sound Localisation

DOA Estimation in Car for Abnormal Sound Localisation

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