Blind source separation based on independent low-rank matrix analysis with sparse regularization for time-series activity

Mitsui, Yoshiki; Kitamura, Daichi; Takamichi, Shinnosuke; Saruwatari, Hiroshi

doi:10.1109/icassp.2017.7951790

Cited by 20 publications

(11 citation statements)

References 22 publications

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“…1. Widely used approach is the separate NMF formulation of rij,m for each of the M sources [12,14,15]. Henceforth, this approach will be referred to as ILRMA and formulated as…”

Section: Ilrma: Separate and Unified Source Modelsmentioning

confidence: 99%

A Unified Bayesian Source Modelling for Determined Blind Source Separation

Narisetty

2019

Interspeech 2019

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This paper proposes a determined blind source separation (BSS) method with a Bayesian generalization for unified modelling of multiple audio sources. Our probabilistic framework allows a flexible multi-source modelling where the number of latent features required for the unified model is optimally estimated. When partitioning the latent features of the unified model to represent individual sources, the proposed approach helps to avoid over-fitting or under-fitting the correlations among sources. This adaptability of our Bayesian generalization therefore adds flexibility to conventional BSS approaches, where the number of latent features in the unified model has to be specified in advance. In the task of separating speech mixture signals, we show that our proposed method models diverse sources in a flexible manner and markedly improves the separation performance as compared to the conventional methods.

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“…1. Widely used approach is the separate NMF formulation of rij,m for each of the M sources [12,14,15]. Henceforth, this approach will be referred to as ILRMA and formulated as…”

Section: Ilrma: Separate and Unified Source Modelsmentioning

confidence: 99%

A Unified Bayesian Source Modelling for Determined Blind Source Separation

Narisetty

2019

Interspeech 2019

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“…A multiplicative update accounting for this l 2 norm constraint was proposed in [46,47]. Alternative sparsity promoting penalties were explored in [48,49].…”

Section: Sparsitymentioning

confidence: 99%

Single-Channel Audio Source Separation with NMF: Divergences, Constraints and Algorithms

Févotte

Vincent

Ozerov

2018

Audio Source Separation

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Spectral decomposition by nonnegative matrix factorisation (NMF) has become state-of-the-art practice in many audio signal processing tasks, such as source separation, enhancement or transcription. This chapter reviews the fundamentals of NMF-based audio decomposition, in unsupervised and informed settings. We formulate NMF as an optimisation problem and discuss the choice of the measure of fit. We present the standard majorisation-minimisation strategy to address optimisation for NMF with common β-divergence, a family of measures of fit that takes the quadratic cost, the generalised Kullback-Leibler divergence and the Itakura-Saito divergence as special cases. We discuss the reconstruction of time-domain components from the spectral factorisation and present common variants of NMFbased spectral decomposition: supervised and informed settings, regularised versions, temporal models.

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“…This extension has greatly improved the performance of separation by taking the low-rank time-frequency structure (co-occurrence among the time-frequency bins) of the source signals into account. ILRMA has achieved the state-of-the-art performance and been further developed by several researchers [17][18][19][20][21][22][23][24][25][26][27][28][29]. In this respect, ILRMA can be considered the new standard of the determined BSS algorithms.…”

Section: Introductionmentioning

confidence: 99%

Consistent independent low-rank matrix analysis for determined blind source separation

Kitamura

Yatabe

2020

EURASIP J. Adv. Signal Process.

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Independent low-rank matrix analysis (ILRMA) is the state-of-the-art algorithm for blind source separation (BSS) in the determined situation (the number of microphones is greater than or equal to that of source signals). ILRMA achieves a great separation performance by modeling the power spectrograms of the source signals via the nonnegative matrix factorization (NMF). Such a highly developed source model can solve the permutation problem of the frequency-domain BSS to a large extent, which is the reason for the excellence of ILRMA. In this paper, we further improve the separation performance of ILRMA by additionally considering the general structure of spectrograms, which is called consistency, and hence, we call the proposed method Consistent ILRMA. Since a spectrogram is calculated by an overlapping window (and a window function induces spectral smearing called main- and side-lobes), the time-frequency bins depend on each other. In other words, the time-frequency components are related to each other via the uncertainty principle. Such co-occurrence among the spectral components can function as an assistant for solving the permutation problem, which has been demonstrated by a recent study. On the basis of these facts, we propose an algorithm for realizing Consistent ILRMA by slightly modifying the original algorithm. Its performance was extensively evaluated through experiments performed with various window lengths and shift lengths. The results indicated several tendencies of the original and proposed ILRMA that include some topics not fully discussed in the literature. For example, the proposed Consistent ILRMA tends to outperform the original ILRMA when the window length is sufficiently long compared to the reverberation time of the mixing system.

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Blind source separation based on independent low-rank matrix analysis with sparse regularization for time-series activity

Cited by 20 publications

References 22 publications

A Unified Bayesian Source Modelling for Determined Blind Source Separation

A Unified Bayesian Source Modelling for Determined Blind Source Separation

Single-Channel Audio Source Separation with NMF: Divergences, Constraints and Algorithms

Consistent independent low-rank matrix analysis for determined blind source separation

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