CONVOLUTION KERNEL COMPENSATION APPLIED TO 1D AND 2D BLIND SOURCE SEPARATION

Damjan Zazula, Aleš Holobar, Matjaž Divjak

Abstract

Many practical situations can be modelled with multiple-input multiple-output (MIMO) models. If the input sources are mutually orthogonal, several blind source separation methods can be used to reconstruct the sources and model transfer channels. In this paper, we derive a new approach of this kind, which is based on the compensation of the model convolution kernel. It detects the triggering instants of individual sources, and tolerates their non-orthogonalities and high amount of additive noise, which qualifies the method in several signal and image analysis applications where other approaches fail.. We explain how to implement the convolution kernel compensation (CKC) method both in 1D and 2D cases. This unified approach made us able to demonstrate its performance in two different experiments. A 1D application was introduced to the decomposition of surface electromyograms (SEMG). Nine healthy males participated in the tests with 5% and 10% maximum voluntary isometric contractions (MVC) of biceps brachii muscle. We identified 3.4 ± 1.3 (mean ± standard deviation) and 6.2 ± 2.2 motor units (MUs) at 5% and 10% MVC, respectively. At the same time, we applied the 2D version of CKC to range imaging. Dealing with the Middlebury Stereo Vision referential set of images, our method found correct matches of 91.3 ± 12.1% of all pixels, while the obtained RMS disparity difference was 3.4 ± 2.5 pixels. This results are comparable to other ranging approaches, but our solution exhibits better robustness and reliability.

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Paper Citation


in Harvard Style

Zazula D., Holobar A. and Divjak M. (2006). CONVOLUTION KERNEL COMPENSATION APPLIED TO 1D AND 2D BLIND SOURCE SEPARATION . In Proceedings of the International Conference on Signal Processing and Multimedia Applications - Volume 1: SIGMAP, (ICETE 2006) ISBN 978-972-8865-64-1, pages 126-133. DOI: 10.5220/0001568801260133


in Bibtex Style

@conference{sigmap06,
author={Damjan Zazula and Aleš Holobar and Matjaž Divjak},
title={CONVOLUTION KERNEL COMPENSATION APPLIED TO 1D AND 2D BLIND SOURCE SEPARATION},
booktitle={Proceedings of the International Conference on Signal Processing and Multimedia Applications - Volume 1: SIGMAP, (ICETE 2006)},
year={2006},
pages={126-133},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001568801260133},
isbn={978-972-8865-64-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Signal Processing and Multimedia Applications - Volume 1: SIGMAP, (ICETE 2006)
TI - CONVOLUTION KERNEL COMPENSATION APPLIED TO 1D AND 2D BLIND SOURCE SEPARATION
SN - 978-972-8865-64-1
AU - Zazula D.
AU - Holobar A.
AU - Divjak M.
PY - 2006
SP - 126
EP - 133
DO - 10.5220/0001568801260133