Subband Splitting: Simple, Efficient and Effective Technique for Solving Block Permutation Problem in Determined Blind Source Separation

Abstract

Solving the permutation problem is essential for determined blind source separation (BSS). Existing methods, such as independent vector analysis (IVA) and independent low-rank matrix analysis (ILRMA), tackle the permutation problem by modeling the co-occurrence of the frequency components of source signals. One of the remaining challenges in these methods is the block permutation problem, which may lead to poor separation results. In this paper, we propose a simple and effective technique for solving the block permutation problem. The proposed technique splits the entire frequencies into overlapping subbands and sequentially applies a BSS method (e.g., IVA, ILRMA, or any other method) to each subband. Since the problem size is reduced by the splitting, the BSS method can effectively work in each subband. Then, the permutations between the subbands are aligned by using the separation result in one subband as the initial values for the other subbands. Experimental results showed that the proposed technique remarkably improved the separation performance without increasing the total computational cost.