Separability of convolutive mixtures: application to the separation of combustion noise and piston-slap in diesel engine

Authors:
Moussa Akil;Christine Servière
Affiliations:
Laboratoire des Images et des Signaux, ENSIEG, Saint-Martin d’Hères, France;Laboratoire des Images et des Signaux, ENSIEG, Saint-Martin d’Hères, France
Venue:
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Year:
2006

Citing 2
Cited 0

Independent component analysis, a new concept?

Signal Processing - Special issue on higher order statistics
A general approach for mutual information minimization and its application to blind source separation

Signal Processing - Special issue: Information theoretic signal processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

We focus on convolutive mixtures, expressed in time-domain. Separation is known to be obtained by testing the independence between delayed outputs. This criterion can be much simplified and we prove in this paper that testing the independence between the contributions of all sources on the same sensor at same time index also leads to separability. We recover the contribution by using Wiener filtering (or Minimal Distorsion Principal) which is included in the separation filters. The independence is tested here with the mutual information. It is minimized only for non-delayed outputs of the Wiener filters. The test is easier and shows good results on simulation and experimental signals for the separation of piston slap and combustion in diesel engine.