Online blind source separation based on time-frequency sparseness

Authors:
Benedikt Loesch;Bin Yang
Affiliations:
Chair of System Theory and Signal Processing, University of Stuttgart, Germany;Chair of System Theory and Signal Processing, University of Stuttgart, Germany
Venue:
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Year:
2009

Citing 0
Cited 4

Blind source separation based on time-frequency sparseness in the presence of spatial aliasing

LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
Adaptive time-domain blind separation of speech signals

LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
A general framework for online audio source separation

LVA/ICA'12 Proceedings of the 10th international conference on Latent Variable Analysis and Signal Separation
Online blind speech separation using multiple acoustic speaker tracking and time-frequency masking

Computer Speech and Language

Quantified Score

Hi-index	0.00

Visualization

Abstract

Recently, blind source separation (BSS) has been proposed to separate signals recorded by a microphone array in a reverberant environment. This paper deals with BSS of a time-varying number of moving sources, which often occurs in practical situations. We develop two online algorithms based on time-frequency (TF) sparseness that are able to deal with moving sources: A block-online algorithm that estimates the number of sources and a gradient-based online algorithm with prespecified maximum number of sources. Both algorithms are evaluated in simulations and real-world scenarios and show good separation performance.