Regularized Fast Multichannel Nonnegative Matrix Factorization with ILRMA-Based Prior Distribution of Joint-Diagonalization Process

Kamo, Keigo; Kubo, Yasunori; Takamune, Norihiro; Kitamura, Daichi; Saruwatari, Hiroshi; Takahashi, Yu; Kondo, Kazunobu

doi:10.1109/icassp40776.2020.9054603

Cited by 3 publications

(2 citation statements)

References 22 publications

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“…To estimate a non-singular matrix called diagonalizer used for jointly diagonalizing the SCMs of sources at each frequency bin, we used a convergence-guaranteed IP method as in FastCTF [37], while a fixed point iteration (FPI) method without convergence guarantee was used in [20]. The diagonalizer can be estimated with a regularization technique that assumes the diagonalizer to be distributed around a demixing matrix estimated by ILRMA [40].…”

Section: B Bss Methods Based On Full-rank Spatial Modelsmentioning

confidence: 99%

Fast Multichannel Nonnegative Matrix Factorization With Directivity-Aware Jointly-Diagonalizable Spatial Covariance Matrices for Blind Source Separation

Sekiguchi

Bando

Nugraha

et al. 2020

IEEE/ACM Trans. Audio Speech Lang. Process.

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This paper describes a computationally-efficient blind source separation (BSS) method based on the independence, lowrankness, and directivity of the sources. A typical approach to BSS is unsupervised learning of a probabilistic model that consists of a source model representing the time-frequency structure of source images and a spatial model representing their interchannel covariance structure. Building upon the low-rank source model based on nonnegative matrix factorization (NMF), which has been considered to be effective for inter-frequency source alignment, multichannel NMF (MNMF) assumes source images to follow multivariate complex Gaussian distributions with unconstrained full-rank spatial covariance matrices (SCMs). An effective way of reducing the computational cost and initialization sensitivity of MNMF is to restrict the degree of freedom of SCMs. While a variant of MNMF called independent low-rank matrix analysis (ILRMA) severely restricts SCMs to rank-1 matrices under an idealized condition that only directional and lessechoic sources exist, we restrict SCMs to jointly-diagonalizable yet full-rank matrices in a frequency-wise manner, resulting in FastMNMF1. To help inter-frequency source alignment, we then propose FastMNMF2 that shares the directional feature of each source over all frequency bins. To explicitly consider the directivity or diffuseness of each source, we also propose rankconstrained FastMNMF that enables us to individually specify the ranks of SCMs. Our experiments showed the superiority of FastMNMF over MNMF and ILRMA in speech separation and the effectiveness of the rank constraint in speech enhancement.

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Section: B Bss Methods Based On Full-rank Spatial Modelsmentioning

confidence: 99%

Fast Multichannel Nonnegative Matrix Factorization With Directivity-Aware Jointly-Diagonalizable Spatial Covariance Matrices for Blind Source Separation

Sekiguchi

Bando

Nugraha

et al. 2020

IEEE/ACM Trans. Audio Speech Lang. Process.

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show abstract

“…It is widely known that the priori information of the source model plays an important role in improving MBSS performance [28,29,19]. While they have been widely studied, the MNMF model given in ( 4) and the ILRMA model given in (6) did not consider the sparse structure of the source prior information in the source model.…”

Section: Cost Functionmentioning

confidence: 99%

Effects of Shear Layer Characteristics on Acoustic Propagation and Source Localization

et al. 2019

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The shear layer characteristics of an open-jet acoustic wind tunnel are of key importance on measurements of aeroacoustics. The effects of thickness, spreading angle and strength of shear layer on acoustic propagation and source localization are investigated through the mean/spreading shear layer with a self-similar velocity distribution. Based on the shear flow, the acoustic propagation is computed by the linearized Euler equations via a source term, and then source localization is obtained from beamforming technique combined with the theory of Amiet. Results show that the numerical method can precisely capture the refraction and reflection after sound traversing shear layer. The thickness, spreading angle and strength of the shear layer exerts little effects on the refracted region where sound wave nearly vertical incident, while mainly influence the corresponding up/downstream region in terms of phase change. Increment of thickness, spreading angle and strength of the shear layer increases the acoustic difference between the shear layer with and without thickness, and produces a larger error of source localization downstream of the actual position.

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Minimum-Volume Multichannel Nonnegative Matrix Factorization for Blind Audio Source Separation

Wang

Guan

Liu

et al. 2021

IEEE/ACM Trans. Audio Speech Lang. Process.

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Regularized Fast Multichannel Nonnegative Matrix Factorization with ILRMA-Based Prior Distribution of Joint-Diagonalization Process

Cited by 3 publications

References 22 publications

Fast Multichannel Nonnegative Matrix Factorization With Directivity-Aware Jointly-Diagonalizable Spatial Covariance Matrices for Blind Source Separation

Fast Multichannel Nonnegative Matrix Factorization With Directivity-Aware Jointly-Diagonalizable Spatial Covariance Matrices for Blind Source Separation

Effects of Shear Layer Characteristics on Acoustic Propagation and Source Localization

Minimum-Volume Multichannel Nonnegative Matrix Factorization for Blind Audio Source Separation

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