Modifications of FastICA in Convolutive Blind Source Separation

TL;DR

This paper proposes modifications to FastICA for convolutive blind source separation, simplifying prewhitening and using SVD for constraints, with simulations verifying improved performance.

Contribution

It introduces a simplified FastICA-based method for convolutive BSS, addressing prewhitening and constraint implementation challenges.

Findings

Effective prewhitening on convolutive mixtures

Contrast function optimized via SVD

Numerical simulations confirm improved separation

Abstract

Convolutive blind source separation (BSS) is intended to recover the unknown components from their convolutive mixtures. Contrary to the contrast functions used in instantaneous cases, the spatial-temporal prewhitening stage and the para-unitary filters constraint are difficult to implement in a convolutive context. In this paper, we propose several modifications of FastICA to alleviate these difficulties. Our method performs the simple prewhitening step on convolutive mixtures prior to the separation and optimizes the contrast function under the diagonalization constraint implemented by single value decomposition (SVD). Numerical simulations are implemented to verify the performance of the proposed method.