Algorithms for Independent Low-Rank Matrix Analysis
نویسندگان
چکیده
This document summarizes an algorithm for independent low-rank matrix analysis, which was proposed as determined rank-1 multichannel nonnegative matrix factorization in the following published papers: Daichi Kitamura, Nobutaka Ono, Hiroshi Sawada, Hirokazu Kameoka, and Hiroshi Saruwatari, “Efficient multichannel nonnegative matrix factorization exploiting rank-1 spatial model,” Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2015), pp. 276–280, April 2015. Daichi Kitamura, Nobutaka Ono, Hiroshi Sawada, Hirokazu Kameoka, and Hiroshi Saruwatari, “Determined blind source separation unifying independent vector analysis and nonnegative matrix factorization,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 24, no. 9, pp. 1626–1641, September, 2016 (open access). Two detailed algorithms for implementation and some empirical knowledges for the use of them are described in this document.
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