2002Unpublished venueRequires access

Pre-filtering non-stationary signals to improve blind source separation

Allan Kardec Barros, Noboru Ohnishi

Open publisher page 2 citations

Abstract

Much work has been carried out concerning blind source separation (BSS) that uses the principle of independent component analysis (ICA), which stands as an elegant, simple and powerful tool to BSS. However, the algorithms previously proposed are still sensitive to nonstationarity, which alters in a bad sense the separation. We propose here a simple pre-processing to the mixed signals based on low-pass filtering that ensures a much better performance for the ICA algorithms.

About this research paper

What this paper is about

Much work has been carried out concerning blind source separation (BSS) that uses the principle of independent component analysis (ICA), which stands as an elegant, simple and powerful tool to BSS. However, the algorithms previously proposed are still sensitive to nonstationarity, which alters in a bad sense the separation. We propose here a simple pre-processing to the mixed signals based on low-pass filtering that ensures a much better performance for the ICA algorithms.

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OpenAlex reports 2 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

Much work has been carried out concerning blind source separation (BSS) that uses the principle of independent component analysis (ICA), which stands as an elegant, simple and powerful tool to BSS. However, the algorithms previously proposed are still sensitive to nonstationarity, which alters in a bad sense the separation. We propose here a simple pre-processing to the mixed signals based on low-pass filtering that ensures a much better performance for the ICA algorithms.

Key concepts: Independent component analysis, Blind signal separation, Computer science, Source separation, Simple (philosophy), Component (thermodynamics), Signal processing, Separation (statistics)

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