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Erschienen in:
Single-Channel Audio Source Separation with NMF: Divergences, Constraints and Algorithms Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

6. Determined Blind Source Separation with Independent Low-Rank Matrix Analysis

verfasst von : Daichi Kitamura, Nobutaka Ono, Hiroshi Sawada, Hirokazu Kameoka, Hiroshi Saruwatari

Erschienen in: Audio Source Separation

Verlag: Springer International Publishing

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Abstract

In this chapter, we address the determined blind source separation problem and introduce a new effective method of unifying independent vector analysis (IVA) and nonnegative matrix factorization (NMF). IVA is a state-of-the-art technique that utilizes the statistical independence between source vectors. However, since the source model in IVA is based on a spherically symmetric multivariate distribution, IVA cannot utilize the characteristics of specific spectral structures such as various sounds appearing in music signals. To solve this problem, we introduce NMF as the source model in IVA to capture the spectral structures. Since this approach is a natural extension of the source model from a vector to a low-rank matrix represented by NMF, the new method is called independent low-rank matrix analysis (ILRMA). We also reveal the relationship between IVA, ILRMA, and multichannel NMF (MNMF), namely, IVA and ILRMA are identical to a special case of MNMF, which employs a rank-1 spatial model. Experimental results show the efficacy of ILRMA compared with IVA and MNMF in terms of separation accuracy and convergence speed.

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Vorheriges Kapitel General Formulation of Multichannel Extensions of NMF Variants
Nächstes Kapitel Deep Neural Network Based Multichannel Audio Source Separation
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Metadaten
Titel
Determined Blind Source Separation with Independent Low-Rank Matrix Analysis
verfasst von
Daichi Kitamura
Nobutaka Ono
Hiroshi Sawada
Hirokazu Kameoka
Hiroshi Saruwatari
Copyright-Jahr
2018
Buch
Audio Source Separation

Print ISBN: 978-3-319-73030-1

Electronic ISBN: 978-3-319-73031-8

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-73031-8_6

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