Modified subspace method based on convex model for underdetermined blind speech separation

Abstract

Recently, many researches have been done to solve the speech separation problems in the underdetermined cases, and the Two-step method is widely used, which estimates the mixing matrix first and then separates the sources. Subspace projection is an effective method for signal separation, whereas it has some limitations because it fixes the active number at the Time-Frequency (TF) point as a constant. A commonly used solution is to add an extra step to estimate the active source number at every TF point, which will increase the computation cost greatly. This paper provides a new convex-model-based subspace projection method with enhanced functionality, which can be used for Underdetermined Blind Source Separation (UBSS). The model takes into account both projection and size of the signal's subspace, without estimating the real source numbers at the TF points. Simulation results show that the proposed method overcomes the shortage of conventional subspace method and achieves higher separation performance. Furthermore, the proposed algorithm can be employed as a preprocessing technology in the fields of acoustic signals enhancement, recognition, and biomedical images, etc.