A new proof of a contrast function for bounded component analysis and further analysis. (September 2018)

• Record Type: Journal Article
• Authors: Gao, Wei; Fan, Shen; Togneri, Roberto; Sreeram, Victor
• Is Part Of: Computer speech & language. Volume 51(2018)
• Page Start: 93
• Page End: 109
• Publication Date: 2018-09
• Subjects: Bounded component analysis -- Blind source separation -- Independent component analysis -- Elementary matrix -- Convex geometry -- Audio signals; Speech processing systems -- Periodicals; Automatic speech recognition -- Periodicals; Computers -- Periodicals; Linguistics -- Periodicals; Speech-Language Pathology -- Periodicals; Traitement automatique de la parole -- Périodiques; Reconnaissance automatique de la parole -- Périodiques; Automatic speech recognition; Speech processing systems; Electronic journals; Periodicals; 006.454
• DOI: 10.1016/j.csl.2018.03.002
• Languages: English
• ISSNs: 0885-2308

Abstract

Abstract: Bounded Component Analysis (BCA) solves the Blind Source Separation (BSS) problem based on geometric assumptions. This paper introduces a new proof of a BCA contrast function, derived from elementary matrices, Gauss–Jordan elimination and convex geometry. The new proof and further analysis provide additional insight into a key assumption of BCA. In addition, an interpretation is presented to clarify one of the limitations of the instantaneous BCA algorithm. Experiments on audio sources support our analysis.