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Shinichi MOGAMI Yoshiki MITSUI Norihiro TAKAMUNE Daichi KITAMURA Hiroshi SARUWATARI Yu TAKAHASHI Kazunobu KONDO Hiroaki NAKAJIMA Hirokazu KAMEOKA
In this letter, we propose a new blind source separation method, independent low-rank matrix analysis based on generalized Kullback-Leibler divergence. This method assumes a time-frequency-varying complex Poisson distribution as the source generative model, which yields convex optimization in the spectrogram estimation. The experimental evaluation confirms the proposed method's efficacy.
Hirokazu KAMEOKA Misa SATO Takuma ONO Nobutaka ONO Shigeki SAGAYAMA
This paper deals with the problem of underdetermined blind source separation (BSS) where the number of sources is unknown. We propose a BSS approach that simultaneously estimates the number of sources, separates the sources based on the sparseness of speech, estimates the direction of arrival of each source, and performs permutation alignment. We confirmed experimentally that reasonably good separation was obtained with the present method without specifying the number of sources.