Independent Low-Rank Matrix Analysis Based on Generalized Kullback-Leibler Divergence

Mogami, Shinichi; Mitsui, Yoshiki; Takamune, Norihiro; Kitamura, Daichi; Saruwatari, Hiroshi; Takahashi, Yu; Kondo, Kazunobu; Nakajima, Hiroaki; Kameoka, Hirokazu

doi:10.1587/transfun.e102.a.458

Cited by 5 publications

(3 citation statements)

References 17 publications

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“…The motivation for selecting these methods is twofold: (1) As shown in Fig. 2, two originally different methods, independent component analysis (ICA) [3,4,[22][23][24][25][26][27][28][29] and nonnegative matrix factorization (NMF) [30][31][32][33][34][35][36], have historically been extended to independent vector analysis (IVA) [37][38][39][40][41][42][43][44][45][46] and multichannel NMF [47][48][49][50][51][52][53][54], respectively, which have recently been unified as independent low-rank matrix analysis (ILRMA) [55][56][57][58][59][60]. (2) The objective functions used in these methods can effectively be minimized by majorization-minimization algorithms with appropriately designed auxiliary functions [36,[61][62]…”

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confidence: 99%

A review of blind source separation methods: two converging routes to ILRMA originating from ICA and NMF

Sawada

Ono

Kameoka

et al. 2019

SIP

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This paper describes several important methods for the blind source separation of audio signals in an integrated manner. Two historically developed routes are featured. One started from independent component analysis and evolved to independent vector analysis (IVA) by extending the notion of independence from a scalar to a vector. In the other route, nonnegative matrix factorization (NMF) has been extended to multichannel NMF (MNMF). As a convergence point of these two routes, independent low-rank matrix analysis has been proposed, which integrates IVA and MNMF in a clever way. All the objective functions in these methods are efficiently optimized by majorization-minimization algorithms with appropriately designed auxiliary functions. Experimental results for a simple two-source two-microphone case are given to illustrate the characteristics of these five methods.

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confidence: 99%

A review of blind source separation methods: two converging routes to ILRMA originating from ICA and NMF

Sawada

Ono

Kameoka

et al. 2019

SIP

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“…B LIND source separation (BSS) [1] is a technique for separating an observed multichannel signal, which is a mixture of multiple sources, into each source without any prior information about the sources or the mixing system. In a determined or overdetermined situation (number of sensors ≥ number of sources), frequency-domain independent component analysis (FDICA) [2]- [4], independent vector analysis (IVA) [5]- [7], and independent low-rank matrix analysis (ILRMA) [8]- [13] have been proposed for audio BSS problems. In particular, ILRMA assumes low-rankness for the power spectrogram of each source using nonnegative matrix factorization (NMF) [14], [15] in addition to statistical independence between sources, and achieves efficient and accurate separation [8].…”

Section: Introductionmentioning

confidence: 99%

Blind Speech Extraction Based on Rank-Constrained Spatial Covariance Matrix Estimation With Multivariate Generalized Gaussian Distribution

Kubo

Takamune

Kitamura

et al. 2020

IEEE/ACM Trans. Audio Speech Lang. Process.

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In this article, we propose a new blind speech extraction (BSE) method that robustly extracts a directional speech from background diffuse noise by combining independent low-rank matrix analysis (ILRMA) and efficient rank-constrained spatial covariance matrix (SCM) estimation. To achieve more accurate BSE than ILRMA, which assumes each source to be a point source (rank-1 spatial model), the proposed method restores the lost spatial basis for the full-rank SCM of diffuse noise. We adopt the multivariate complex generalized Gaussian distribution (GGD) as the statistical generative model to express various types of observed signal. To estimate the model parameters for an arbitrary shape parameter of the multivariate GGD, we derive a new inequality for rankconstrained SCMs. Also, we propose new acceleration methods to accomplish much faster extraction than conventional blind source separation methods. In BSE experiments using simulated and real recorded data, we confirm that the proposed method achieves more accurate and faster speech extraction than conventional methods.

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“…Blind source separation (BSS) [1] is a technique that separates an observed multichannel signal into each source signal without any prior information about each source or the mixing system. In a determined or overdetermined situation (number of sensors ≥ number of sources), frequency-domain independent component analysis (FDICA) [2]- [4], independent vector analysis (IVA) [5]- [7], and independent low-rank matrix analysis (ILRMA) [8]- [12] have been proposed for audio BSS problems.…”

Section: Introductionmentioning

confidence: 99%

Acceleration of rank-constrained spatial covariance matrix estimation for blind speech extraction

Kubo

Takamune

Kitamura

et al. 2019

2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)

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In this paper, we propose new accelerated update rules for rank-constrained spatial covariance model estimation, which efficiently extracts a directional target source in diffuse background noise. The naive update rule requires heavy computation such as matrix inversion or matrix multiplication. We resolve this problem by expanding matrix inversion to reduce computational complexity; in the parameter update step, we need neither matrix inversion nor multiplication. In an experiment, we show that the proposed accelerated update rule achieves 87 times faster calculation than the naive one.

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Independent Low-Rank Matrix Analysis Based on Generalized Kullback-Leibler Divergence

Cited by 5 publications

References 17 publications

A review of blind source separation methods: two converging routes to ILRMA originating from ICA and NMF

A review of blind source separation methods: two converging routes to ILRMA originating from ICA and NMF

Blind Speech Extraction Based on Rank-Constrained Spatial Covariance Matrix Estimation With Multivariate Generalized Gaussian Distribution

Acceleration of rank-constrained spatial covariance matrix estimation for blind speech extraction

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