Multichannel Speech Separation and Enhancement Using the Convolutive Transfer Function

Xiaofei Li 1 Laurent Girin 2, 1 Sharon Gannot 3 Radu Horaud 1
1 PERCEPTION - Interpretation and Modelling of Images and Videos
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
2 GIPSA-CRISSP - CRISSP
GIPSA-DPC - Département Parole et Cognition
Abstract : This paper addresses the problem of speech separation and enhancement from multichannel convolutive and noisy mixtures, \emph{assuming known mixing filters}. We propose to perform the speech separation and enhancement task in the short-time Fourier transform domain, using the convolutive transfer function (CTF) approximation. Compared to time-domain filters, CTF has much less taps, consequently it has less near-common zeros among channels and less computational complexity. The work proposes three speech-source recovery methods, namely: i) the multichannel inverse filtering method, i.e. the multiple input/output inverse theorem (MINT), is exploited in the CTF domain, and for the multi-source case, ii) a beamforming-like multichannel inverse filtering method applying single source MINT and using power minimization, which is suitable whenever the source CTFs are not all known, and iii) a constrained Lasso method, where the sources are recovered by minimizing the $\ell_1$-norm to impose their spectral sparsity, with the constraint that the $\ell_2$-norm fitting cost, between the microphone signals and the mixing model involving the unknown source signals, is less than a tolerance. The noise can be reduced by setting a tolerance onto the noise power. Experiments under various acoustic conditions are carried out to evaluate the three proposed methods. The comparison between them as well as with the baseline methods is presented.
Type de document :
Pré-publication, Document de travail
Submitted to IEEE/ACM Transactions on Audio, Speech and Language Processing. 2018
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https://hal.inria.fr/hal-01799809
Contributeur : Team Perception <>
Soumis le : vendredi 25 mai 2018 - 10:47:42
Dernière modification le : mardi 10 juillet 2018 - 01:18:41

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  • HAL Id : hal-01799809, version 1
  • ARXIV : 1711.07911

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Xiaofei Li, Laurent Girin, Sharon Gannot, Radu Horaud. Multichannel Speech Separation and Enhancement Using the Convolutive Transfer Function. Submitted to IEEE/ACM Transactions on Audio, Speech and Language Processing. 2018. 〈hal-01799809〉

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