A sparse component model of source signals and its application to blind source separation

In this paper, we propose a new method of blind source separation (BSS) for music signals. Our method has the following characteristics: 1) the method is a combination of the sparseness-based model of source signals and the factorized basis model in nonnegative matrix factorization (NMF), 2) it is assumed that only one basis which structure source signals is active at each time-frequency bin of the observed signals, in order to degrade the degree of freedom, 3) parameter estimation algorithm is based on the EM algorithm regarding the index of the only one active basis as the hidden variable. We develop the formulation at a different point from NMF and show source separation performance in some simulation experiments.

• Type: Journal
• Year: 2010
• Where: ICASSP
• Authors: Yu Kitano, Hirokazu Kameoka, Yosuke Izumi, Nobutaka Ono, Shigeki Sagayama
• Keywords: Blind Source Separation, ICASSP 2010, Signal Processing, Source Separation, Source Signals