Bayesian Nonparametric Approach to Blind Separation of Infinitely Many Sparse Sources
Hirokazu Kameoka, Misa Sato, Takuma Ono, Nobutaka Ono, Shigeki Sagayama · IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences · 2013
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.