Adaptive segmentation and separation of determined convolutive mixtures under dynamic conditions

Authors:
Benedikt Loesch;Bin Yang
Affiliations:
University of Stuttgart;University of Stuttgart
Venue:
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
Year:
2010

Citing 2
Cited 3

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
Performance measurement in blind audio source separation

IEEE Transactions on Audio, Speech, and Language Processing

The 2010 signal separation evaluation campaign (SiSEC2010): audio source separation

LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
Multi-source TDOA estimation in reverberant audio using angular spectra and clustering

Signal Processing
The 2011 signal separation evaluation campaign (SiSEC2011): - audio source separation -

LVA/ICA'12 Proceedings of the 10th international conference on Latent Variable Analysis and Signal Separation

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we propose a method for blind source separation (BSS) of convolutive audio recordings with short blocks of stationary sources, i.e. dynamically changing source activity but no source movements. It consists of a time-frequency sparseness based localization step to identify segments with stationary sources whose number is equal to the number of microphones. We then use a frequency domain independent component analysis (ICA) algorithm that is robust to short data segments to separate each identified segment. In each segment we solve the permutation problem using the state coherence transform (SCT). Experimental results using real room impulse responses show a good separation performance.