# MM Algorithms for Joint Independent Subspace Analysis with Application to Blind Single and Multi-Source Extraction

@article{Scheibler2020MMAF, title={MM Algorithms for Joint Independent Subspace Analysis with Application to Blind Single and Multi-Source Extraction}, author={Robin Scheibler and Nobutaka Ono}, journal={ArXiv}, year={2020}, volume={abs/2004.03926} }

In this work, we propose efficient algorithms for joint independent subspace analysis (JISA), an extension of independent component analysis that deals with parallel mixtures, where not all the components are independent. We derive an algorithmic framework for JISA based on the majorization-minimization (MM) optimization technique (JISA-MM). We use a well-known inequality for super-Gaussian sources to derive a surrogate function of the negative log-likelihood of the observed data. The…

## 8 Citations

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### Over-determined Speech Source Separation and Dereverberation

- Physics2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)
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It is revealed that an orthogonal constraint enables efficient update of a noise reduction filter in the proposed framework similar to the previously proposed over-determined speech source separation case.

### Over-Determined Semi-Blind Speech Source Separation

- Physics2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)
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We propose a semi-blind speech source separation that jointly optimizes several acoustic functions, i.e., speech source separation (SS), dereverberation (DR), acoustic echo reduction (AE), and…

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