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| Adapted Stopping Residue Error Based Sparse Representation for Speech Denoising |
| ZHOU Weili HE Qianhua WANG Yalou PANG Wenfeng |
| (School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510640, China) |
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Abstract A sparse representation speech denoising method based on adapted stopping residue error is proposed. Firstly, an over complete dictionary of the clean speech power spectrum is learned by the K-Singular Value Decomposition (K-SVD) algorithm. In the sparse representation stage, the stopping residue error is adaptively achieved according to the estimated cross terms and the noise spectrum which is adjusted by a weighted factor, and the Orthogonal Matching Pursuit (OMP) approach is applied to reconstruct the clean speech spectrum from the noisy speech. Finally, the clean speech is re-synthesis via the inverse Fourier transform with the reconstructed speech spectrum and the noisy speech phase. The experiment results show that the proposed method outperforms the standard spectral subtraction, sparse representation based speech denoising algorithm and the AutoRegressive Hidden Markov Model (AR-HMM) based speech denoising method in terms of subjective and objective measure.
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Received: 18 April 2016
Published: 21 October 2016
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| Fund: The National Natural Science Foundation of China (61571192), The Science and Technology Foundation of Guangdong Province (2015A010103003) |
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Corresponding Authors:
HE Qianhua
E-mail: eeqhhe@scut.edu.cn
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