Everton Z. Nadalin

Learn More
In this work, we investigate the application of the recently introduced signal decomposition method known as robust principal component analysis (RPCA) to the problem of wave separation in seismic data. The motivation of our research comes from the observation that the elements of the decomposition performed by RPCA can be associated with particular(More)
This work fits in the frames of sparse component analysis (SCA), informed source separation (ISS) and doping water-marking. The SCA relies on a strong hypothesis of sparsity of the sources. In a particular context where the original sources are available (ISS), we make the distributions of the time-frequency coefficients of the sources more sparse, through(More)
This paper proposes the use of the clustering method called Weighted Fuzzy C-means to solve the problem of mixing matrix estimation in underdetermined source separation based on sparse component analysis. The performed comparative analysis shows that the approach has a significant application potential, especially if the distributions of the columns of the(More)
The separation of an underdetermined audio mixture can be performed through sparse component analysis (SCA) that relies however on the strong hypothesis that source signals are sparse in some domain. To overcome this difficulty in the case where the original sources are available before the mixing process, the informed source separation (ISS) embeds in the(More)