Speech Enhancement Based on Sparse Representation Using Joint Dictionary

DOI

10.2991/csece-18.2018.109How to use a DOI?

Keywords

speech denoising; composite dictionary; K-SVD algorithm; speech enhancement; LARC algorithm

Abstract

A new speech denoising method that aims for processing corrupted speech signal which is based on the sparse representation theory of speech signal. In this paper, we train a composite dictionary consisting of the concatenation of the speech dictionary and the noise dictionary by using the K-SVD algorithm. Noise is divided into structured and unstructured noise in this paper. For structured noise, we train speech and noise dictionary firstly, and then according to the different coherence between speech and noise, we use LARC algorithm with a suitably chosen residual coherence threshold to realize the separation of the speech and the noise. For unstructured noise, we only need speech dictionary to extract the clean speech from corrupted speech. Experiments indicate that the proposed method gives better enhancement results in terms of quality measures of speech. The proposed method outperforms the universal dictionary speech enhancement algorithm.

Copyright

© 2018, 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  - Ming Wei
AU  - Zheng Liu
AU  - Xueqin Chen
AU  - Heming Zhao
PY  - 2018/02
DA  - 2018/02
TI  - Speech Enhancement Based on Sparse Representation Using Joint Dictionary
BT  - Proceedings of the 2018 International Conference on Computer Science, Electronics and Communication Engineering (CSECE 2018)
PB  - Atlantis Press
SP  - 500
EP  - 503
SN  - 2352-538X
UR  - https://doi.org/10.2991/csece-18.2018.109
DO  - 10.2991/csece-18.2018.109
ID  - Wei2018/02
ER  -