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.
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Rivet, B., Vigneron, V., Paraschiv-Ionescu, A., Jutten, C. (2004). Wavelet De-noising for Blind Source Separation in Noisy Mixtures. In: Puntonet, C.G., Prieto, A. (eds) Independent Component Analysis and Blind Signal Separation. ICA 2004. Lecture Notes in Computer Science, vol 3195. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30110-3_34
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DOI: https://doi.org/10.1007/978-3-540-30110-3_34
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