Blind Source Separation by Multiresolution Analysis using AMUSE Algorithm
DOI:
https://doi.org/10.33837/msj.v1i3.106Abstract
Algorithms for blind source separation have been extensively studied in the last years. This paper proposes the use of multiresolution analysis in three decomposition levels of the wavelet transform, such as a preprocessing step, and the AMUSE algorithm to separate the source signals in distinct levels of resolution. The results show that there is an improvement in the estimation of the signals and the of mixing matrix even in noisy environment if compared to the use of AMUSE only.References
CHICHOCKI, A.; AMARI, S. Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications. West Sussex: Wiley, 2003.
COMON, P.; JUTTEN, C. Handbook of Blind Source Separation: Independent Component Analysis and Applications. Oxford: Elsevier, 2010.
DAUBECHIES, I. Ten Lectures on Wavelets. CBMS-NSF regional conference series in applied mathematics, Philadelphia, 1992.
HYVÄRINEN, A.; KARHUNEN, J.; OJA, E. Independent Component Analysis. New York: Wiley, 2001.
HUANG, R.; CHEUNG, Y.; ZHU, S. A Parallel Architecture Using Discrete Wavelet Transform for Fast ICA Implementation. IEEE Int. Conf. Neural Networks & Signal Processing on, v. 14-17, p. 1358-1361, 2003.
KAWAGUCHI, A.; TRUONG, Y. K.; HUANG, X. Application of Polynomial Spline Independent Component Analysis to fMRI Data. In: NAIK, G. R. Independent Component Analysis for Audio and Biosignal Applications. Rijeka: InTech, 2012. p. 209-220.
LEO, M.; D'ORAZIO, T.; DISTANTE, A. Feature extraction for automatic ball recognition: comparison between wavelet and ICA preprocessing. Image and Signal Processing and Analysis, ISPA 2003. Proceedings of the 3rd International Symposium on, vol. 2, p. 587-592, 2003.
LÓ, P. M. G.; LOZANO, H. M.; SÁNCHEZ, F. L. P.; MORENO, L. N. O. Blind Source Separation of audio signals using independent component analysis and wavelets. Electrical Communications and Computers CONIELECOMP 21st International Conference on, p.152-157, 2011.
MISSAOUI, I.; LACHIRI, Z. Blind speech separation based on undecimated wavelet packet-perceptual filterbanks and independent component analysis. IJCSI International Journal of Computer Science Issues on, v. 8, 2011.
MIJOVIC, B.; VOS, M. D.; GLIGORIJEVIC, I.; TAELMAN, J.; HUFFEL, S. V. Using Wavelet Transformation in Blind Sources Separation of the Fetal Electrocardiogram. Majlesi Journal of Electrical Engineering on, v. 5, p. 2188-2196, 2011.
TALBI, M.; AICHA, A. B.; SALHI, L.; CHERIF, A. Bionic Wavelet Based Denoising Using Source Separation. Int J Comput Commun on, v. 7, p. 574-585, 2012.
RADU, M.; HULLE, M. M. V. A comparative survey on adaptive neural network algorithms for independent component analysis. Romanian Reports in Physics on, v. 55.1, p. 49-74, 2003.
SHAYESTEH, M.; FALLAHIAN, J. Source Separation From Single-Channel Recordings by Combining Empirical-Mode Decomposition and Independent Component Analysis. IEEE Transactions on Biomedical Enginnering on, v. 57, p. 33-37, 2010.
STRANG, G.; NGUYEN, T. Wavelets and Filter Banks. Cambridge: Wellesley, 1997.
TONG, L.; LIU, R.; SOON, V. C.; HUANG, Y. Indeterminacy and identifiability of blind identification. Circuits and Systems, IEEE Transactions on, v. 38, p. 499-509, 1991.
YEREDOR, A. Second-order methods based on color. In: COMON, P.; JUTTEN, C. Handbook of Blind Source Separation: Independent Component Analysis and Applications. Oxford: Elsevier, 2010. p. 227-278.
WEEKS , M. Digital Signal Processing using MATLAB and Wavelets. Massachusetts: Jones and Bartlett Publishers, 2007.
Downloads
Published
How to Cite
Issue
Section
License
Authors who publish in this journal agree to the following terms:
a) The Authors retain the copyright and grant the journal the right to first publication, with the work simultaneously licensed under the Creative Commons Attribution License that allows the sharing of the work with acknowledgment of authorship and initial publication in this journal.
b) Authors are authorized to assume additional contracts separately, for non-exclusive distribution of the version of the work published in this journal (eg, publishing in institutional repository or as a book chapter), with acknowledgment of authorship and initial publication in this journal.
c) Authors are allowed and encouraged to publish and distribute their work online (eg in institutional repositories or on their personal page) at any point before or during the editorial process, as this can generate productive changes, as well as increase impact and citation of the published work.
