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Speech Enhancement Based on Bayesian Low-Rank and Sparse Decomposition of Multichannel Magnitude Spectrograms

Authors :
Bando, Yoshiaki
Itoyama, Katsutoshi
Konyo, Masashi
Tadokoro, Satoshi
Nakadai, Kazuhiro
Yoshii, Kazuyoshi
Kawahara, Tatsuya
Okuno, Hiroshi G.
Source :
IEEE-ACM Transactions on Audio, Speech, and Language Processing; February 2018, Vol. 26 Issue: 2 p215-230, 16p
Publication Year :
2018

Abstract

This paper presents a blind multichannel speech enhancement method that can deal with the time-varying layout of microphones and sound sources. Since nonnegative tensor factorization (NTF) separates a multichannel magnitude (or power) spectrogram into source spectrograms without phase information, it is robust against the time-varying mixing system. This method, however, requires prior information such as the spectral bases (templates) of each source spectrogram in advance. To solve this problem, we develop a Bayesian model called robust NTF (Bayesian RNTF) that decomposes a multichannel magnitude spectrogram into target speech and noise spectrograms based on their sparseness and low rankness. Bayesian RNTF is applied to the challenging task of speech enhancement for a microphone array distributed on a hose-shaped rescue robot. When the robot searches for victims under collapsed buildings, the layout of the microphones changes over time and some of them often fail to capture target speech. Our method robustly works under such situations, thanks to its characteristic of time-varying mixing system. Experiments using a 3-m hose-shaped rescue robot with eight microphones show that the proposed method outperforms conventional blind methods in enhancement performance by the signal-to-noise ratio of 1.03 dB.

Details

Language :
English
ISSN :
23299290
Volume :
26
Issue :
2
Database :
Supplemental Index
Journal :
IEEE-ACM Transactions on Audio, Speech, and Language Processing
Publication Type :
Periodical
Accession number :
ejs44294764
Full Text :
https://doi.org/10.1109/TASLP.2017.2772340