Similarity Search-based Blind Source Separation

Abstract

In this paper, we propose a new method for blind source separation, where we perform similarity search for a prepared clean speech database. The purpose of this mechanism is to separate short utterances that we frequently encounter in a real-world situation. The new method employs a local Gaussian model (LGM) for the probability density functions of separated signals, and updates the LGM variance parameters by using the similarity search results. Experimental results show that the method performed very well in an ideal situation where we employ a close database that contains the source components used for the mixtures. In more realistic situations where an open database was used, the separation performance degraded to a certain degree, but was still better than existing methods.