Blind source separation with low frequency compensation for convolutive mixtures

Authors:
Xiaoming Zhu;Keshab K. Parhi;Warren J. Warwick
Affiliations:
University of Minnesota, Twin Cities, Department of Electrical and Computer Engineering, Minneapolis, MN;University of Minnesota, Twin Cities, Department of Electrical and Computer Engineering, Minneapolis, MN;Department of Pediatrics, Minneapolis, MN
Venue:
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
Year:
2009

Citing 7
Cited 0

An information-maximization approach to blind separation and blind deconvolution

Neural Computation
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications

Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Independent Component Analysis: A Tutorial Introduction

Independent Component Analysis: A Tutorial Introduction
Blind Source Separation Using Temporal Predictability

Neural Computation
A Class of Frobenius Norm-Based Algorithms Using Penalty Term and Natural Gradient for Blind Signal Separation

IEEE Transactions on Audio, Speech, and Language Processing
Multichannel blind deconvolution for source separation in convolutive mixtures of speech

IEEE Transactions on Audio, Speech, and Language Processing
Fast and robust fixed-point algorithms for independent component analysis

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper addresses the blind source separation of convolutive and temporally correlated voice mixtures. We combine natural gradient algorithm and temporal complexity algorithm to preserve the temporal and frequency structures of the original signals. Due to the underlying scaling constraint of natural gradient algorithm, the low frequency components of the original sources are suppressed in the output signals. To compensate for low frequency loss, we use a measure of temporal complexity to recover the low frequency components of the source signals. Simulation results show that the proposed algorithm can well preserve the structure of the original signals both in time and frequency domains.