文章基本信息

标题：Speech Denoising via Low-Rank and Sparse Matrix Decomposition
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作者：Huang, Jianjun ; Zhang, Xiongwei ; Zhang, Yafei 等
期刊名称：ETRI Journal
印刷版ISSN：1225-6463
电子版ISSN：2233-7326
出版年度：2014
卷号：36
期号：1
页码：167-170
DOI：10.4218/etrij.14.0213.0033
语种：English
出版社：Electronics and Telecommunications Research Institute
摘要：In this letter, we propose an unsupervised framework for speech noise reduction based on the recent development of low-rank and sparse matrix decomposition. The proposed framework directly separates the speech signal from noisy speech by decomposing the noisy speech spectrogram into three submatrices: the noise structure matrix, the clean speech structure matrix, and the residual noise matrix. Evaluations on the Noisex-92 dataset show that the proposed method achieves a signal-to-distortion ratio approximately 2.48 dB and 3.23 dB higher than that of the robust principal component analysis method and the non-negative matrix factorization method, respectively, when the input SNR is -5 dB.
关键词：Low-rank and sparse matrix decomposition;noise reduction;robust principal component analysis