International audienceThis short paper presents an efficient, flexible implementation of the SRP-PHAT multichannel sound source localization method. The method is evaluated on the single-source tasks of the LOCATA 2018 development dataset, and an associated Matlab toolbox is made available online

Bertin, Nancy

Camberlein, Ewen

Deleforge, Antoine

Di Carlo, Diego

Gaultier, Clément

Lebarbenchon, Romain

arXiv

HAL-CentraleSupelec

Evaluation of an Open-Source Implementation of the SRP-PHAT Algorithm within the 2018 Locata Challenge

HAL-Rennes 1

Archive Ouverte en Sciences de l'Information et de la Communication

INRIA a CCSD electronic archive server

HAL Id: hal-02187964https://hal.archives-ouvertes.fr/hal-02187964Submitted on 18 Jul 2019HAL is a multi-disciplinary open accessarchive for the deposit and dissemination of sci-entific research documents, whether they are pub-lished or not. The documents may come fromteaching and research institutions in France orabroad, or from public or private research centers.L’archive ouverte pluridisciplinaire HAL, estdestinée au dépôt et à la diffusion de documentsscientifiques de niveau recherche, publiés ou non,émanant des établissements d’enseignement et derecherche français ou étrangers, des laboratoirespublics ou privés.Evaluation of an Open-Source Implementation of theSRP-PHAT Algorithm within the 2018 Locata ChallengeRomain Lebarbenchon, Ewen Camberlein, Diego Di Carlo, Clément Gaultier,Antoine Deleforge, Nancy BertinTo cite this version:Romain Lebarbenchon, Ewen Camberlein, Diego Di Carlo, Clément Gaultier, Antoine Deleforge, etal.. Evaluation of an Open-Source Implementation of the SRP-PHAT Algorithm within the 2018Locata Challenge. LOCATA Challenge Workshop, a satellite event of IWAENC 2018, Sep 2018,Tokyo, Japan. ￿hal-02187964￿LOCATA Challenge Workshop, a satellite event of IWAENC 2018 September 17-20, 2018, Tokyo, JapanEVALUATION OF AN OPEN-SOURCE IMPLEMENTATION OF THE SRP-PHATALGORITHM WITHIN THE 2018 LOCATA CHALLENGERomain Lebarbenchon1, Ewen Camberlein1, Diego di Carlo1,Clément Gaultier1, Antoine Deleforge2, Nancy Bertin11Univ Rennes 1, Inria, CNRS, IRISA, F-35000 Rennes, France2Université de Lorraine, CNRS, Inria, Loria, F-54000 Nancy, FranceABSTRACTThis short paper presents an efficient, flexible implementation of theSRP-PHAT multichannel sound source localization method. Themethod is evaluated on the single-source tasks of the LOCATA 2018development dataset, and an associated Matlab toolbox is madeavailable online.1. INTRODUCTIONMany source localization methods are based on the estimationof time-difference-of-arrival (TDOA) between microphones. Inthe two-microphone case, the thorough evaluation carried out in[1] highlighted the good performance of the Generalized Cross-Correlation with PHAse Transform (GCC-PHAT) algorithm [2]among several other methods. A general principle for extending thismethod to the multichannel setting, named SRP-PHAT, was pro-posed in [3]. However, no reference implementation of this familyof algorithms (including all its possible variants) was available todate, to the best of the authors’ knowledge. In particular, choices ofparameters (pooling in time and frequency, grid search resolution)and management of the multiple coordinate systems between pairsof microphones and the whole antenna have not been systematicallydocumented.We present here our participation to the 2018 LOCATA chal-lenge [4]. This aims at evaluating our implementation of SRP-PHAT, whose good performance was already probed in realistic en-vironments in [5], but which was not confronted to other methodson a common, realistic dataset so far. It is here applied to task 1 (sin-gle, static source and static array), task 3 (single moving source andstatic array) and task 5 (single moving source and moving array) ofthe challenge. Evaluation of the method on the development datasetshows encouraging results, confirming the legitimate place of SRP-PHAT among state-of-the-art methods for multichannel source lo-calization in reverberant environments.2. PRINCIPLES2.1. TDOA and angular spectrumThe general principle of SRP-PHAT is to compute a functionΦ(θ, ϕ) termed “angular spectrum”, where θ and φ are azimuth andelevation variables, which is expected to exhibit local maxima in thedirections of active sources. More precisely, the following steps arefollowed:• build in each time-frequency (t, f) bin a local angular spec-trum function φ(t, f, θ, ϕ) that is large for directions (θ, ϕ)which are compatible with the observed signal at (t, f) andsmall otherwise,• integrate (or pool) this function over the time-frequency plane,leading to a global angular spectrum,• find the peaks of this spectrum above a certain threshold anddistant from a certain minimum angle.2.2. GCC-PHATThe GCC algorithm [2] generalizes cross-correlation by multiply-ing the cross-spectral density (CSD) of two signals by complexweights in the frequency domain. The PHAT weighting consists ofnormalizing the CSD to have unit amplitude at all frequencies, thusconsidering phase differences only and minimizing the influence ofthe source’s power spectral density. In the case of a single broad-band source, two microphones and under far-field (source-array dis-tance much larger that the array aperture h), free-field (only the di-rect source-to-microphone sound paths are considered) and noise-less assumptions, the asymptotic GCC-PHAT of the microphonesignals in the discrete frequency domain is e−2iπfτ cos(α) where fis the frequency index, α is the angle of arrival (AOA) of the sourcesignal at the array and τ is the maximum observable delay in sam-ples. We have τ = hFs/C whereC denotes the speed of sound andFs is the frequency of sampling. This leads to the following naturallocal angular spectrum for a microphone pair:φGCC−PHAT(t, f, α) = <(x1(t, f)x∗2(t, f)|x1(t, f)x∗2(t, f)|e−2iπfτ cos(α))(1)where x1, x2 denote the microphone signals in the short-timeFourier transform (STFT) domain and α is a local azimuth in thecoordinate system defined by the microphone pair. Note that thisspectrum does not depend on elevation.2.3. Extension to multichannelSRP-PHAT’s principle is to first compute local angular spectra foreach microphone pair, then to aggregate them across pairs (bringingthem back in the global coordinate system) before being pooled andmaximized. The computation consists of the following steps:1. Define the search space, i.e. a grid of possible DOAs(θj , ϕk) for which we want to evaluate Φ in the global coor-dinate system;2. For each microphone pair n:LOCATA Challenge Workshop, a satellite event of IWAENC 2018 September 17-20, 2018, Tokyo, Japan(a) Compute the corresponding AOAs {α(n)jk }jk with re-spect to the microphone pair;(b) Resample {α(n)jk }jk into a smaller set {α(n)i }i in orderto reduce computational time;(c) Compute the GCC-PHAT local angular spectrum φnat angles {α(n)i }i for microphone pair n according toformula (1);(d) Linearly interpolate φn back to the original angle res-olution and global coordinate system;3. Compute the global spectrum Φ(θj , ϕk) by pooling the localangular spectra φn over all time-frequency bins (t, f) andacross all microphone pairs n. Pooling methods (such asmaximum or sum) and their order over each of the f, t, nindexes must be chosen for this purpose.4. Find the indexes j and k of the largest peak (single sourcecase) or peaks (multiple source case) of Φ(θj , ϕk), yieldingthe estimated source azimuth(s) θj and elevation(s) ϕk.A Matlab toolbox allowing easy and flexible implementationsof SRP-PHAT as well as 7 other angular spectrum-based was madefreely available online under the name Multichannel BSS Locate1.3. EXPERIMENTS AND RESULTSWe now evaluate our implementation of SRP-PHAT on tasks 1, 3and 5 of the LOCATA challenge [4] with the robot head and theEigenmike arrays for azimuth and elevation estimation. The othertwo antennas of the challenge are discarded since they are near-linear (preventing elevation estimation). Moreover, DICIT is in-compatible with our far-field assumption while the dummy head isincompatible with our free-field assumption. All microphones (12)and all microphone pairs (66) are used with the robot head. All mi-crophones are also used with the Eigenmike, but microphone pairswith a curvilinear distance lesser than 90◦ are discarded in orderto reduce the overall algorithm complexity, resulting in 240 micro-phone pairs usage instead of the 496 available pairs. A sphere sam-pled with 1◦ resolution both in azimuth and elevation is used as thesearch space (see Sec. 2.3 - 1). For each microphone pair, the eval-uated AOAs (see Sec. 2.3 - 2.(b)) are computed with 5◦ resolution.SRP-PHAT is applied every 256 ms to 512 ms signals (downsam-pled to 16 kHz) using an overlapping sliding analysis window. TheSTFT is applied to each channel of the signals using 64 ms Fourierframes (1024 samples) with 50% overlap and sine windows. Thisresults in 15 Fourier frames per analysis window. The pooling meth-ods used are first summations over microphone pairs and frequen-cies and then maximum over time frames. Only the largest value1http://bass-db.gforge.inria.fr/bss_locate/over the global angular search space is returned as a DOA estimate,as only single source localization is considered. DOA estimates arelinearly interpolated over time in order to match the one-estimate-per-time-stamp requirement of LOCATA.Table 1. shows localization errors obtained with the proposedmethod. Encouragingly, mean azimuth errors (first row) are 2 to 6times smaller than those reported for the baseline MUSIC method[4] for the same arrays and tasks. To reduce the effect of gross errorson reported mean values, average successful localization errors fordifferent success threshold are also reported. The proposed methodlocalizes the target source with less than 20◦ error in at least 92% ofthe tests for all tasks and arrays. Nearly 100% correct localization isachieved in the static scenario (task 1). While the robot head arrayenables better performance in all tasks, average errors never exceed10◦ regardless of the task and array.4. CONCLUSIONIn this paper, we reported competitive single sound source localiza-tion results using the SRP-PHAT implementation of the Multichan-nel BSS Locate toolbox on the 2018 LOCATA challenge dataset.The toolbox’s flexibility allows one to easily adapt the method toarbitrary array geometries, to various types of emitted signals andenvironments, and to easily generalize any two-channel localizationmethod to more channels. It can also be tuned to find a trade-off be-tween computational time and accuracy, although we focused onaccuracy for this challenge. While the toolbox can output multiplesource direction estimates for a given input signal, it does not yetincorporate source counting or source tracking solutions, prevent-ing its use in multiple sources scenario. Such extensions will beconsidered for future releases of the toolbox.5. REFERENCES[1] C. Blandin, A. Ozerov, and E. Vincent, “Multi-source TDOA estima-tion in reverberant audio using angular spectra and clustering,” SignalProcessing, vol. 92, no. 8, pp. 1950–1960, 2012.[2] C. Knapp and G. Carter, “The generalized correlation method for es-timation of time delay,” IEEE Transactions on Acoustics, Speech, andSignal Processing, vol. 24, no. 4, pp. 320–327, August 1976.[3] J. Dibiase, H. Silverman, and M. Brandstein, “Robust localization in re-verberant rooms,” in Microphone Arrays: Signal Processing Techniquesand Applications. Springer, 2001, pp. 157–180.[4] H. W. Löllmann, C. Evers, A. Schmidt, H. Mellmann, H. Barfuss,P. A. Naylor, and W. Kellermann, “The locata challenge data corpusfor acoustic source localization and tracking,” in IEEE Sensor Arrayand Multichannel Signal Processing Workshop (SAM),(Sheffield, UK),2018.[5] N. Bertin, E. Camberlein, R. Lebarbenchon, E. Vincent,S. Sivasankaran, I. Illina, and F. Bimbot, “Voicehome-2, an ex-tended corpus for multichannel speech processing in real homes,” toappear in Speech Communication, Elsevier, 2018.Table 1: Sound localization results obtained on the LOCATA development dataset using the proposed SRP-PHAT method. For each taskand array, different success thresholds (in degrees) are considered. A localization is considered successful when both azimuth and elevationestimation errors are below these thresholds. The percentage of successful localizations (suc.) and their average azimuth (az.) and elevation(el.) errors in degrees are showed for each threshold.SuccessthresholdTask 1 Task 3 Task 5Robot head Eigenmike Robot head Eigenmike Robot head Eigenmikeaz. | el. | suc. az. | el. | suc. az. | el. | suc. az. | el. | suc. az. | el. | suc. az. | el. | suc.No thresh. 1.51|1.71| − 7.04|4.68| − 4.43|2.66| − 8.79|4.41| − 6.19|3.16| − 9.31|4.37| −20◦ 1.43|1.66|99.9 6.95|4.64|99.9 2.48|1.75|95.8 7.82|3.12|92.5 1.76|1.83|94.5 5.84|2.94|94.810◦ 1.43|1.66|99.9 6.95|4.64|99.9 2.35|1.56|93.7 6.27|2.40|71.0 1.64|1.77|93.3 5.43|2.80|89.5

Evaluation of an Open-Source Implementation of the SRP-PHAT Algorithm within the 2018 Locata Challenge

Abstract

Similar works

Full text

Available Versions

HAL-CentraleSupelec

HAL-Rennes 1

Archive Ouverte en Sciences de l'Information et de la Communication

INRIA a CCSD electronic archive server