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Minimum-volume Multichannel Nonnegative matrix factorization for blind source separation

2021-01-16 08:12:23
Jianyu Wang, Shanzheng Guan, Xiao-Lei Zhang
arXiv_SD
     
Abstract

Multichannel blind source separation aims to recover the latent sources from their multichannel mixture without priors. A state-of-art blind source separation method called independent low-rank matrix analysis (ILRMA) unified independent vector analysis (IVA) and nonnegative matrix factorization (NMF). However, speech spectra modeled by NMF may not find a compact representation and it may not guarantee that each source is identifiable. To address the problem, here we propose a modified blind source separation method that enhances the identifiability of the source model. It combines ILRMA with penalty item of volume constraint. The proposed method is optimized by standard majorization-minimization framework based multiplication updating rule, which ensures the stability of convergence. Experimental results demonstrate the effectiveness of the proposed method compared with AuxIVA, MNMF and ILRMA.

Abstract (translated)

URL

https://arxiv.org/abs/2101.06398

PDF

https://arxiv.org/pdf/2101.06398.pdf

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