Blind Audio Source Separation Using Independent Component Analysis and Independent Vector Analysis

Alyaa MAHDI; Ahmet ELBIR; Fethullah KARABIBER

doi:10.18100/ijamec.270075

Research ArticlesverifiedPeer-ReviewedOpen Access

Pages: 174-177

Blind Audio Source Separation Using Independent Component Analysis and Independent Vector Analysis

Alyaa MAHDI •

Ahmet ELBIR •

Fethullah KARABIBER

Publication DateDecember 1, 2016

Volume / IssueVol. , No. (pp. 174-177)

DOI Identifier10.18100/ijamec.270075

subject

Abstract

Blind Source Separation (BSS) is one of the most important and challenging problem for the researchers in audio and speech processing area. In the literature, many different methods have been proposed to solve BSS problem. In this study, we have compared the performance of three popular BSS methods based on Independent Component Analysis (ICA) and Independent Vector Analysis Models, which are Fast-ICA, Kernel-ICA and Fast-IVA. We collected experimental data by recording speech from 13 people. Three different scenarios are proposed to compare the performance of BSS methods effectively. Experimental results show that the Fast-IVA has better performance than the ICA based methods according to performance metrics of Source-to-Artifact Ratio, Source-to-Distortion Ratio and Source-to-Noise Ratio. But ICA methods give better results than Fast-IVA according to the Source-to-Interference Ratio.

Keywords:Blind source separationIndependent component analysis

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Blind Audio Source Separation Using Independent Component Analysis and Independent Vector Analysis

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References (15)

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