A New Sparse Representation Algorithm for Speech Denoising

DOI

10.2991/csss-14.2014.29How to use a DOI?

Keywords

speech denoising, spectrogram, K-SVD algorithm, redundant dictionary, sparse representation

Abstract

This paper proposes a new speech denoising method that uses K-SVD sparse representation algorithm. This approach is based on sparse and redundant representation over dictionary. Here, spectrogram patches are used as training samples for the initial redundant dictionary. However, since the K-SVD algorithm is limited in handling small size spectrogram, the training samples need to extend their deployment to arbitrary spectrogram sizes by defining a global spectrogram prior that forces sparsity over patches in every location in the spectrogram. Simulation experiments show that the performance of the proposed K-SVD denoising algorithm is stable, and the white noise can be effectively separated. In addition, K-SVD algorithm is a simple and effective algorithm which surpasses the redundant DCT method and Gabor dictionary. In a word, K-SVD algorithm leads to an alternative speech denoising method.

Copyright

© 2014, 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  - Zhou Yan
PY  - 2014/06
DA  - 2014/06
TI  - A New Sparse Representation Algorithm for Speech Denoising
BT  - Proceedings of the 3rd International Conference on Computer Science and Service System
PB  - Atlantis Press
SP  - 131
EP  - 134
SN  - 1951-6851
UR  - https://doi.org/10.2991/csss-14.2014.29
DO  - 10.2991/csss-14.2014.29
ID  - Yan2014/06
ER  -