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Least-Squares Error Beamforming Using Minimum Statistics and Multichannel Frequency-Domain Adaptive Filtering (2003)
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BibTeX
@MISC{Aichner03least-squareserror,
author = {Robert Aichner and Wolfgang Herbordt and Herbert Buchner and Walter Kellermann},
title = {Least-Squares Error Beamforming Using Minimum Statistics and Multichannel Frequency-Domain Adaptive Filtering},
year = {2003}
}
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Abstract
In this paper we introduce a novel adaptive beamformer which also copes with incoherent background noise. After derivation of the optimum filter based on a weighted time-domain least-squares error criterion we present an efficient realization by applying a multichannel frequency-domain algorithm exhibiting RLS-like convergence. For the computation of this algorithm a simultaneous estimation of the power spectral density matrices of both, the noise signal and the noisy speech signal is necessary. Hence, we propose to use a novel approach based on minimum statistics to achieve this simultaneous estimation. Furthermore, the necessary estimate of a desired signal is generated by using single-channel spectral subtraction. The musical noise is avoided in our approach due to the inherent temporal and spatial averaging of our proposed beamformer. Experimental results show that the algorithm is well-suited for diffuse noise environments (e.g. car noise). Moreover, subjective listening tests confirm that a high speech quality can be obtained. 1.
Keyphrases
multichannel frequency-domain adaptive filtering simultaneous estimation optimum filter spatial averaging weighted time-domain least-squares error criterion novel approach multichannel frequency-domain efficient realization necessary estimate high speech quality noisy speech signal power spectral density matrix rls-like convergence minimum statistic desired signal car noise novel adaptive beamformer subjective listening test diffuse noise environment single-channel spectral subtraction musical noise noise signal incoherent background noise experimental result
