MATEC Web Conf.
Volume 128, 20172017 International Conference on Electronic Information Technology and Computer Engineering (EITCE 2017)
|Number of page(s)||4|
|Section||Signal & Image Processing|
|Published online||25 October 2017|
Speech Denoising in White Noise Based on Signal Subspace Low-rank Plus Sparse Decomposition
1 Science and Technology on Avionics Integration Laboratory, Shanghai, China
2 Nanchang Hangkong University, Nanchang, 330063 China
a Corresponding author: firstname.lastname@example.org
In this paper, a new subspace speech enhancement method using low-rank and sparse decomposition is presented. In the proposed method, we firstly structure the corrupted data as a Toeplitz matrix and estimate its effective rank for the underlying human speech signal. Then the low-rank and sparse decomposition is performed with the guidance of speech rank value to remove the noise. Extensive experiments have been carried out in white Gaussian noise condition, and experimental results show the proposed method performs better than conventional speech enhancement methods, in terms of yielding less residual noise and lower speech distortion.
Key words: speech enhancement / subspace method / low-rank plus sparse decomposition
© The authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (http://creativecommons.org/licenses/by/4.0/).
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