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• W3008184518 abstract "In this paper, we address a blind source separation (BSS) problem and propose a new extended framework of independent positive semidefinite tensor analysis (IPSDTA). IPSDTA is a state-of-the-art BSS method that enables us to take interfrequency correlations into account, but the generative model is limited within the multivariate Gaussian distribution and its parameter optimization algorithm does not guarantee stable convergence. To resolve these problems, first, we propose to extend the generative model to a parametric multivariate Student's t distribution that can deal with various types of signal. Secondly, we derive a new parameter optimization algorithm that guarantees the monotonic nonincrease in the cost function, providing stable convergence. Experimental results reveal that the cost function in the conventional IPSDTA does not display monotonically nonincreasing properties. On the other hand, the proposed method guarantees the monotonic nonincrease in the cost function and outperforms the conventional ILRMA and IPSDTA in the source-separation performance." @default.
• W3008184518 created "2020-03-06" @default.
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• W3008184518 date "2020-02-20" @default.
• W3008184518 modified "2023-10-02" @default.
• W3008184518 title "Convergence-guaranteed Independent Positive Semidefinite Tensor Analysis Based on Student's t Distribution" @default.
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• W3008184518 doi "https://doi.org/10.48550/arxiv.2002.08582" @default.
• W3008184518 hasPublicationYear "2020" @default.
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