Underdetermined Blind Source Separation from Time-delayed Mixtures Based on Prior Information Exploitation

Abstract

-Recently, many researches have been done to solve the challenging problem of Blind Source Separation (BSS) problems in the underdetermined cases, and the "Two-step" method is widely used, which estimates the mixing matrix first and then extracts the sources. To estimate the mixing matrix, conventional algorithms such as Single-Source-Points (SSPs) detection only exploits the sparsity of original signals. This paper proposes a new underdetermined mixing matrix estimation method for time-delayed mixtures based on the receiver prior exploitation. The prior information is extracted from the specific structure of the complex-valued mixing matrix, which is used to derive a special criterion to determine the SSPs. Moreover, after selecting the SSPs, Agglomerative Hierarchical Clustering (AHC) is used to automaticly cluster, suppress, and estimate all the elements of mixing matrix. Finally, a convex-model based subspace method is applied for signal separation. Simulation results show that the proposed algorithm can estimate the mixing matrix and extract the original source signals with higher accuracy especially in low SNR environments, and does not need the number of sources before hand, which is more reliable in the real non-cooperative environment.

Sparse Component Analysis (SCA) Based on Adaptive Time—Frequency Thresholding for Underdetermined Blind Source Separation (UBSS)

“…If the signal in the TF plane is not sufficiently sparse or if noises exist after the transformation, there will be some outliers present. To solve either of these issues and boost the estimated performance of the mixing matrix, several estimation techniques based on the detection of single-source points (SSPs) have been developed [ 16 , 24 , 25 , 26 , 27 , 28 , 29 , 30 ].…”

“…For a time-delayed mixing matrix with a single-source point, ref. [ 27 ] showed that the time-delayed mixing model of the UBSS problem works well with the single-source identification-based mixing matrix estimation strategy. The authors of [ 26 ] utilised the single-source detection (SSD) algorithm that recognises the TF points occupied by a single source for each source.…”