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作者:

Bai, Zhigang (Bai, Zhigang.) | Bao, Changchun (Bao, Changchun.) (学者:鲍长春) | Yan, Bofang (Yan, Bofang.)

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EI Scopus

摘要:

In this paper, we present a novel approach for speech enhancement based on nonnegative matrix factorization (NMF) with the speech magnitude spectrum constrained by a codebook. First, we utilize a codebook to model the magnitude spectrum of clean speech and a speech magnitude spectrum codebook is trained containing the priori information of speech. Second, a classic noise estimation algorithm is employed to estimate the power spectral density (PSD) of noise to avoid noise classification. Then, we obtain the basis matrix of the noisy speech by combining the noise spectral with the optimal entry from the speech codebook. The magnitude spectrum of the noisy speech is decomposed by performing NMF and the estimated speech and noise components are obtained. Finally, the obtained speech and noise components are used to enhance the noisy speech. Moreover, the residual noise is further eliminated by applying the speech presence probability (SPP). The objective evaluations demonstrate that the proposed algorithm outperforms the conventional NMF based method for all the evaluated noise types at various input signal-To-noise ratios. © 2018 IEEE.

关键词:

Acoustic noise Factorization Image processing Matrix algebra Signal to noise ratio Spectral density Speech enhancement

作者机构:

  • [ 1 ] [Bai, Zhigang]Faculty of Information Technology, Beijing University of Technology, Speech and Audio Signal Processing Laboratory, Beijing; 100124, China
  • [ 2 ] [Bao, Changchun]Faculty of Information Technology, Beijing University of Technology, Speech and Audio Signal Processing Laboratory, Beijing; 100124, China
  • [ 3 ] [Yan, Bofang]Faculty of Information Technology, Beijing University of Technology, Speech and Audio Signal Processing Laboratory, Beijing; 100124, China

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年份: 2018

页码: 361-365

语种: 英文

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