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

Year 2016, , 174 - 177, 01.12.2016

Alyaa Mahdi , Ahmet Elbir Fethullah Karabiber

https://doi.org/10.18100/ijamec.270075

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 separation, Independent component analysis

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