Feng Li

This book proposes two extensions of the effective optimization algorithms concentrating on RPCA and Fusion-Net for singing voice separation. One is using different weighted value for describing the separated low-rank matrix. The other is exploring rank-1 constraint minimization of singular value in RPCA. In terms of source-to-artifact ratio, the previous is better than the later. However, CRPCA obtains better separation quality than WRPCA in singing voice separation. The outcomes of this research contribute to further improving the technologies related to music information retrieval. Additionally, the potential contribution of this research is to deal with the problems of noise reduction and speech enhancement by using the separated lowrank and sparse model. Since the background noise is assumed as the part of low-rank component and the human speech is regarded as the part of sparse component.

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