Underdetermined Blind Source Separation Method for Speech Signals Based on SOM-DPC and Compressed Sensing

Underdetermined blind source separation has re-ceived increasing attention in recent years as an ef-fective method for speech-signal processing. Hence, a self-organizing mapping-density peak clustering and compressed sensing approach, which is a two-step ap-proach, is proposed herein to improve the accuracy of underdetermined blind source separation. The ap-proach features the following two aspects: (1) A mix-ing matrix estimation method based on self-organizing mapping and density peak clustering, which can in-tuitively determine the number of source signals, re-move outliers, and determine the column vector of the mixing matrix based on local density; (2) a com-pressed sensing-based source signal reconstruction method, which can exploit the sparsity of signals in the frequency domain and use a hierarchical coupling method to reconstruct the source signal accurately and efficiently under the premise that the prior knowl-edge of the signal is unknown. The proposed method does not require the number of source signals and exhibits excellent performance under different noise conditions. Theoretical analysis and experimental re-sults demonstrate the effectiveness of the proposed method.