Independent Low-Rank Matrix Analysis Based on Generalized Kullback-Leibler Divergence
Shinichi Mogami, Yoshiki Mitsui, Norihiro Takamune, Daichi Kitamura, Hiroshi Saruwatari, Yu Takahashi, Kazunobu Kondo, Hiroaki NAKAJIMA, Hirokazu Kameoka · IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences · 2019
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.