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Journals / International Journal of Applied Mathematics Electronics and Computers / 2016 / Special Issue (2016)

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

Alyaa MAHDİ
Ahmet ELBİR
Fethullah KARABİBER
Pages
174–177
Publication Date
2016-12-01
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

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.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Blind Source Separation (BSS) is one of the most important and challenging problem for the researchers in audio and speech processing area.
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Key Findings & Data: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.
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Conclusion & Impact: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.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Blind source separation · Independent component analysis

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.

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Cite This Article

MAHDİ, A., ELBİR, A., KARABİBER, F. (2016). Blind Audio Source Separation Using Independent Component Analysis and Independent Vector Analysis. International Journal of Applied Mathematics Electronics and Computers, Special Issue (2016), 174–177. https://ajindex.com/articles/258178

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