N2 - Subspace-based methods have been effectively used to estimate enhanced speech from noisy speech samples. In the traditional subspace approaches, a critical step is splitting of two invariant subspaces associated with signal and noise via subspace decomposition, which is often performed by singular-value decomposition or eigenvalue decomposition. However, these decomposition algorithms are highly sensitive to the presence of large corruptions, resulting in a large amount of residual noise within enhanced speech in low signal-to-noise ratio (SNR) situations. In this paper, a joint low-rank and sparse matrix decomposition (JLSMD) based subspace method is proposed for speech enhancement. In the proposed method, we firstly structure the corrupted data as a Toeplitz matrix and estimate its effective rank value for the underlying clean speech matrix. Then the subspace decomposition is performed by means of JLSMD, where the decomposed low-rank part corresponds to enhanced speech and the sparse part corresponds to noise signal, respectively. An extensive set of experiments have been carried out for both of white Gaussian noise and real-world noise. Experimental results show that the proposed method performs better than conventional methods in many types of strong noise conditions, in terms of yielding less residual noise and lower speech distortion. L1 - http://www.czasopisma.pan.pl/Content/101345/PDF/aoa-2016-0024.pdf L2 - http://www.czasopisma.pan.pl/Content/101345 PY - 2016 IS - No 2 EP - 254 DO - 10.1515/aoa-2016-0024 KW - subspace speech enhancement KW - singular value decomposition KW - joint low-rank and sparse matrix decomposition A1 - Sun, Chengli A1 - Xie, Jianxiao A1 - Leng, Yan PB - Polish Academy of Sciences, Institute of Fundamental Technological Research, Committee on Acoustics VL - vol. 41 DA - 2016 T1 - A Signal Subspace Speech Enhancement Approach Based on Joint Low-Rank and Sparse Matrix Decomposition SP - 245 UR - http://www.czasopisma.pan.pl/dlibra/publication/edition/101345 T2 - Archives of Acoustics