Multiple Speech Source Separation with Non-Sparse Components Recovery by Using Dual Similarity Determination

Maoshen JIA  Jundai SUN  Feng DENG  Junyue SUN  

IEICE TRANSACTIONS on Information and Systems   Vol.E101-D   No.4   pp.925-932
Publication Date: 2018/04/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2016IIP0019
Type of Manuscript: Special Section PAPER (Special Section on Intelligent Information and Communication Technology and its Applications to Creative Activity Support)
Category: Elemental Technologies for human behavior analysis
blind source separation,  non-sparse component,  similarity coefficient,  intelligent systems,  

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In this work, a multiple source separation method with joint sparse and non-sparse components recovery is proposed by using dual similarity determination. Specifically, a dual similarity coefficient is designed based on normalized cross-correlation and Jaccard coefficients, and its reasonability is validated via a statistical analysis on a quantitative effective measure. Thereafter, by regarding the sparse components as a guide, the non-sparse components are recovered using the dual similarity coefficient. Eventually, a separated signal is obtained by a synthesis of the sparse and non-sparse components. Experimental results demonstrate the separation quality of the proposed method outperforms some existing BSS methods including sparse components separation based methods, independent components analysis based methods and soft threshold based methods.