Perpendicular Cross-Spectra Fusion for Sound Source Localization With a Planar Microphone Array

Multiple sound source localization in reverberant environments stands as one of the most difficult challenges for many applications related to microphone array signal processing. In this paper, we describe perpendicular cross-spectra fusion (PCSF), a new direction-of-arrival (DOA) estimation algorit...

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Published inIEEE/ACM transactions on audio, speech, and language processing Vol. 25; no. 9; pp. 1821 - 1835
Main Authors Stefanakis, Nikolaos, Pavlidi, Despoina, Mouchtaris, Athanasios
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 01.09.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Bins
Computer simulation
Direction of arrival
Direction of arrival estimation
Divergence
Estimation
Estimators
Histograms
information fusion
Localization
Microphone arrays
multiple source localization
Real time
Reliability analysis
Signal processing
Sound localization
source counting
Speech
Time-frequency analysis
Online AccessGet full text
ISSN2329-9290
2329-9304
2329-9304
DOI10.1109/TASLP.2017.2718733

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Abstract Multiple sound source localization in reverberant environments stands as one of the most difficult challenges for many applications related to microphone array signal processing. In this paper, we describe perpendicular cross-spectra fusion (PCSF), a new direction-of-arrival (DOA) estimation algorithm, which utilizes an analytic formula for direction estimation in the time-frequency (TF) domain. Inherent to this technique is the presence of multiple direction estimation subsystems which operate in parallel, producing a multiplicity of candidate DOAs at each TF point. We define a metric of coherence, based on the property of divergence of the different DOA estimators, for assessing the reliability of different signal portions, so that only TF bins with a high quality of directional information are exploited for local DOA estimation. The resulting collection of local DOAs is provided as input to a recently proposed histogram processing approach, which is based on matching pursuit. Results based on simulation and real recordings illustrate the advantages of PCSF compared to other DOA estimation techniques subject to the same histogram-based processing, in the context of real-time multiple source localization and counting; improved performance in reverberant conditions and high tolerance to diffuse and common mode noise.
AbstractList Multiple sound source localization in reverberant environments stands as one of the most difficult challenges for many applications related to microphone array signal processing. In this paper, we describe perpendicular cross-spectra fusion (PCSF), a new direction-of-arrival (DOA) estimation algorithm, which utilizes an analytic formula for direction estimation in the time-frequency (TF) domain. Inherent to this technique is the presence of multiple direction estimation subsystems which operate in parallel, producing a multiplicity of candidate DOAs at each TF point. We define a metric of coherence, based on the property of divergence of the different DOA estimators, for assessing the reliability of different signal portions, so that only TF bins with a high quality of directional information are exploited for local DOA estimation. The resulting collection of local DOAs is provided as input to a recently proposed histogram processing approach, which is based on matching pursuit. Results based on simulation and real recordings illustrate the advantages of PCSF compared to other DOA estimation techniques subject to the same histogram-based processing, in the context of real-time multiple source localization and counting; improved performance in reverberant conditions and high tolerance to diffuse and common mode noise.
Author Mouchtaris, Athanasios
Stefanakis, Nikolaos
Pavlidi, Despoina
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SubjectTerms Bins
Computer simulation
Direction of arrival
Direction of arrival estimation
Divergence
Estimation
Estimators
Histograms
information fusion
Localization
Microphone arrays
multiple source localization
Real time
Reliability analysis
Signal processing
Sound localization
source counting
Speech
Time-frequency analysis
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Title Perpendicular Cross-Spectra Fusion for Sound Source Localization With a Planar Microphone Array
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