Speech Dereverberation Based on Sparse Matrix Decomposition

DOI

10.2991/icitmi-15.2015.196How to use a DOI?

Keywords

RPCA,dereverberation,.sparse matrix,low rank matrix

Abstract

Because of the increasingly demands of high quality audio signal, speech dereverberation,as the preliminary processing of speaker recognition and automatic speech recognition(ASR), becomes more and more important. The speech obtained from microphones is always distorted by reverberation. Conventional approaches always build a model to dereverberate speech. However, in different environments, these models may not be effective. For this reason, we propose an algorithm which does not base on any environment model assumptions so that it can be used for all speech. A piece of clean speech can be represented through a sparse matrix. The reverberated speech matrix can be decomposed into two matrices, clean speech matrix and reverberated noise matrix, to capture the sparse components of the speech using Robust Principal Component Analysis (RPCA). Evaluations via many different criterions show that the new approach preserves the clean speech’s information well and dereverberate the speech well.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Miao Fan
AU  - Liyang Liu
AU  - Weifeng Li
PY  - 2015/10
DA  - 2015/10
TI  - Speech Dereverberation Based on Sparse Matrix Decomposition
BT  - Proceedings of the 4th International Conference on Information Technology and Management Innovation
PB  - Atlantis Press
SP  - 1169
EP  - 1173
SN  - 2352-538X
UR  - https://doi.org/10.2991/icitmi-15.2015.196
DO  - 10.2991/icitmi-15.2015.196
ID  - Fan2015/10
ER  -