An online EM algorithm for source extraction using distributed microphone arrays

Expectation maximization (EM)-based clustering is applied in many recent multichannel source extraction techniques. The estimated model parameters are used to compute time-frequency masks, or estimate second order statistics (SOS) of the source signals. However, in applications with moving sources w...

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Bibliographic Details
Published in21st European Signal Processing Conference (EUSIPCO 2013) pp. 1 - 5
Main Authors Taseska, Maja, Habets, Emanuel A. P.
Format Conference Proceeding
LanguageEnglish
Published EURASIP 01.09.2013
Subjects
Abstracts
Arrays
distributed arrays
expectation maximization
Lead
Microphones
Noise
Noise measurement
online learning
PSD matrix estimation
Simulation
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ISSN2219-5491
2219-5491

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Summary:Expectation maximization (EM)-based clustering is applied in many recent multichannel source extraction techniques. The estimated model parameters are used to compute time-frequency masks, or estimate second order statistics (SOS) of the source signals. However, in applications with moving sources where the model parameters are time-varying, the batch EM algorithm is inapplicable. We propose an online EM-based clustering of position estimates, where the model parameters are estimated adaptively. A direct-to-diffuse ratio-based speech presence probability is used to detect noisy observations and reduce diffuse and spatially incoherent noise. The desired source signal is extracted by a multichannel Wiener filter computed using SOS estimated from the time-varying model parameters. We show that the signal of a moving source can be extracted, while reducing moving interferers and background noise.
ISSN:2219-5491
2219-5491