Abstract
Blind source separation, which supposes that the sources are independent, is a well known domain in signal processing. However, in a noisy environment the estimation of the criterion is harder due to the noise. In strong noisy mixtures, we propose two new principles based on the combination of wavelet de-noising processing and blind source separation. We compare them in the cases of white/correlated Gaussian noise. © Springer-Verlag 2004.
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CITATION STYLE
Rivet, B., Vigneron, V., Paraschiv-Ionescu, A., & Jutten, C. (2004). Wavelet de-noising for blind source separation in noisy mixtures. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3195, 263–270. https://doi.org/10.1007/978-3-540-30110-3_34
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