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检索条件"主题词=positive semidefinite tensor factorization"

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STUDENT'S T NONNEGATIVE MATRIX factorization AND positive semidefinite tensor factorization FOR SINGLE-CHANNEL AUDIO SOURCE SEPARATION 41

STUDENT'S <i>T</i> NONNEGATIVE MATRIX FACTORIZATION AND POSI...

引用

41st IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Yoshii, Kazuyoshi Itoyama, Katsutoshi Goto, Masataka Kyoto Univ Grad Sch Informat Kyoto 6068501 Japan Nat Inst Adv Ind Sci & Technol AIST Tsukuba Ibaraki Japan

ISBN: (纸本)9781479999880

This paper presents a robust variant of nonnegative matrix factorization (NMF) based on complex Student's t distributions (t-NMF) for source separation of single-channel audio signals. The Itakura-Saito divergence NMF (Gaussian NMF) is justified for this purpose under an assumption that the complex spectra of source signals and those of the mixture signal are complex Gaussian distributed (the additivity of power spectra holds). In fact, however, the source spectra are often heavy-tailed distributed. When the source spectra are complex Cauchy distributed, for example, the mixture spectra are also complex Cauchy distributed (the additivity of amplitude spectra holds). Using the complex t distribution that includes the complex Gaussian and Cauchy distributions as its special cases, we propose t-NMF as a unified extension of Gaussian NMF and Cauchy NMF. Furthermore, we propose the corresponding variant of positive semidefinite tensor factorization based on multivariate complex t distributions (t-PSDTF). The experimental results showed that while t-NMF and t-PSDTF were comparative to Gaussian counterparts in terms of peak performance, they worked much better on average because they are insensitive to initialization and tend to avoid local optima.

关键词： Source separation nonnegative matrix factorization positive semidefinite tensor factorization t distribution

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引用

IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Yoshii, Kazuyoshi Kyoto Univ Grad Sch Informat Kyoto Japan RIKEN Ctr Adv Intelligence Project AIP Tokyo Japan

ISBN: (纸本)9781538646588

This paper presents an ultimate extension of nonnegative matrix factorization (NMF) for audio source separation based on full covariance modeling over all the time-frequency (TF) bins of the complex spectrogram of an observed mixture signal. Although NMF has been widely used for decomposing an observed power spectrogram in a TF-wise manner, it has a critical limitation that the phase values of interdependent TF bins cannot be dealt with. This problem has been solved only partially by several phase-aware extensions of NMF that decompose an observed complex spectrogram in an time- and/or frequency-wise manner. In this paper, we propose correlated tensor factorization (CTF) that approximates the full covariance matrix over all TF bins as the sum of the Kronecker products between basis covariance matrices over frequency bands and the corresponding ones over time frames. All the TF bins of the complex spectrogram of each source signal are estimated jointly in an interdependent manner via Wiener filtering. We discuss how to reduce the computational cost of CTF and report the results of comparative evaluation of CTF with its special cases such as NMF and positive semidefinite tensor factorization (PSDTF).

关键词： Source separation nonnegative matrix factorization nonnegative tensor factorization positive semidefinite tensor factorization correlated tensor factorization

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Independent positive semidefinite tensor Analysis in Blind Source Separation 26

Independent Positive Semidefinite Tensor Analysis in Blind S...

引用

European Signal Processing Conference (EUSIPCO)

作者： Ikeshita, Rintaro Hitachi Ltd Res & Dev Grp Tokyo Japan

ISBN: (纸本)9789082797015

The issue of convolutive blind source separation (BSS) is addressed in this paper. Independent low-rank matrix analysis (ILRMA), unifying frequency-domain independent component analysis (FDICA) and nonnegative matrix factorization (NMF), is a method that has recently proposed to model low rank structure of source spectra by using NMF in addition to independence between sources used in FDICA and independent vector analysis (IVA). Although ILRMA has been shown to provide better separation performance than FDICA and IVA, the frequency components of each source are assumed to be independent in ILRMA due to NMF modeling of source spectra, which may degrade its performance when the short-term Fourier transform (STFT) is unable to decorrelate the frequency components for each source. This paper therefore presents a new BSS method that unifies IVA and positive semidefinite tensor factorization (PSDTF). PSDTF models not only power spectra in the same way NMF does but also models the correlations between frequency bins in each source. The proposed method can be viewed as a multichannel extension of PSDTF and exploits both the independence between sources and the inter-frequency correlations as a clue for separating mixtures. Experimental results indicate the improved performance of our approach.

关键词： Blind source separation nonnegative matrix factorization positive semidefinite tensor factorization independent component analysis independent vector analysis

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引用

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Kazuyoshi Yoshii Graduate School of Informatics Kyoto University Japan RIKEN Center for Advanced Intelligence Project (AIP) Japan

ISBN: (纸本)9781538646595

This paper presents an ultimate extension of nonnegative matrix factorization (NMF) for audio source separation based on full covariance modeling over all the time-frequency (TF) bins of the complex spectrogram of an observed mixture signal. Although NMF has been widely used for decomposing an observed power spectrogram in a TF-wise manner, it has a critical limitation that the phase values of interdependent TF bins cannot be dealt with. This problem has been solved only partially by several phase-aware extensions of NMF that decompose an observed complex spectrogram in an time-and/or frequency-wise manner. In this paper, we propose correlated tensor factorization (CTF) that approximates the full covariance matrix over all TF bins as the sum of the Kronecker products between basis covariance matrices over frequency bands and the corresponding ones over time frames. All the TF bins of the complex spectrogram of each source signal are estimated jointly in an interdependent manner via Wiener filtering. We discuss how to reduce the computational cost of CTF and report the results of comparative evaluation of CTF with its special cases such as NMF and positive semidefinite tensor factorization (PSDTF).

关键词： Source separation nonnegative matrix factorization nonnegative tensor factorization positive semidefinite tensor factorization correlated tensor factorization Source separation NMF protocol NFIC gene matrix decomposition Spectrogram factorization Audio tensor Bins variance covariance matrix orthogonality Wiener filters

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A DIAGONAL PLUS LOW-RANK COVARIANCE MODEL FOR COMPUTATIONALLY EFFICIENT SOURCE SEPARATION

A DIAGONAL PLUS LOW-RANK COVARIANCE MODEL FOR COMPUTATIONALL...

引用

27th IEEE International Workshop on Machine Learning for Signal Processing (MLSP)

作者： Liutkus, Antoine Yoshii, Kazuyoshi INRIA Speech Proc Team Rocquencourt France Kyoto Univ RIKEN AIP Kyoto Japan

ISBN: (纸本)9781509063413

This paper presents an accelerated version of positive semidefinite tensor factorization (PSDTF) for blind source separation. PSDTF works better than nonnegative matrix factorization (NMF) by dropping the arguable assumption that audio signals can be whitened in the frequency domain by using short-term Fourier transform (STFT). Indeed, this assumption only holds true in an ideal situation where each frame is infinitely long and the target signal is completely stationary in each frame. PSDTF thus deals with full covariance matrices over frequency bins instead of forcing them to be diagonal as in NMF. Although PSDTF significantly outperforms NMF in terms of separation performance, it suffers from a heavy computational cost due to the repeated inversion of big covariance matrices. To solve this problem, we propose an intermediate model based on diagonal plus low-rank covariance matrices and derive the expectation-maximization (EM) algorithm for efficiently updating the parameters of PSDTF. Experimental results showed that our method can dramatically reduce the complexity of PSDTF by several orders of magnitude without a significant decrease in separation performance.

关键词： Blind source separation nonnegative matrix factorization positive semidefinite tensor factorization low-rank approximation

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