NDLI: Determined blind source separation unifying independent vector analysis and nonnegative matrix factorization

Content Provider	ACM Digital Library
Author	Sawada, Hiroshi Saruwatari, Hiroshi Ono, Nobutaka Kitamura, Daichi Kameoka, Hirokazu
Copyright Year	2016
Abstract	This paper addresses the determined blind source separation problem and proposes a new effective method unifying independent vector analysis (IVA) and nonnegative matrix factorization (NMF). IVA is a state-of-the-art technique that utilizes the statistical independence between sources in a mixture signal, and an efficient optimization scheme has been proposed for IVA. However, since the source model in IVA is based on a spherical multivariate distribution, IVA cannot utilize specific spectral structures such as the harmonic structures of pitched instrumental sounds. To solve this problem, we introduce NMF decomposition as the source model in IVA to capture the spectral structures. The formulation of the proposed method is derived from conventional multichannel NMF (MNMF), which reveals the relationship between MNMF and IVA. The proposed method can be optimized by the update rules of IVA and single-channel NMF. Experimental results show the efficacy of the proposed method compared with IVA and MNMF in terms of separation accuracy and convergence speed.
Starting Page	1622
Ending Page	1637
Page Count	16
File Format	PDF
ISSN	23299290
e-ISSN	23299304
Volume Number	24
Issue Number	9
Journal	IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-09-01
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Blind source separation Determined Independent vector analysis Nonnegative matrix factorization
Content Type	Text
Resource Type	Article
Subject	Instrumentation Computational Mathematics Signal Processing Electrical and Electronic Engineering Acoustics and Ultrasonics Speech and Hearing Media Technology

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