Adapted Stopping Residue Error Based Sparse Representation for Speech Denoising

ZHOU Weili; HE Qianhua; WANG Yalou; PANG Wenfeng

doi:10.11999/JEIT160369

Volume 39 Issue 2

Feb. 2017

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ZHOU Weili, HE Qianhua, WANG Yalou, PANG Wenfeng. Adapted Stopping Residue Error Based Sparse Representation for Speech Denoising[J]. Journal of Electronics & Information Technology, 2017, 39(2): 309-315. doi: 10.11999/JEIT160369

Citation:

ZHOU Weili, HE Qianhua, WANG Yalou, PANG Wenfeng. Adapted Stopping Residue Error Based Sparse Representation for Speech Denoising[J]. Journal of Electronics & Information Technology, 2017, 39(2): 309-315. doi: 10.11999/JEIT160369

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Adapted Stopping Residue Error Based Sparse Representation for Speech Denoising

doi: 10.11999/JEIT160369

Funds:

The National Natural Science Foundation of China (61571192), The Science and Technology Foundation of Guangdong Province (2015A010103003)

Received Date: 2016-04-18
Rev Recd Date: 2016-08-25
Publish Date: 2017-02-19

Abstract

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.
- Speech denoising,
- Sparse representation,
- K-Singular Value Decomposition (K-SVD),
- Orthogonal Matching Pursuit (OMP)

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References(21)

References

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