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In this paper we propose an approach for the problem of single channel source separation of speech and music signals. Our approach is based on representing each source's power spectral density using dictionaries and nonlinearly projecting the mixture signal spectrum onto the combined span of the dictionary entries. We encourage sparsity and continuity of the dictionary coefficients using penalty terms...
In this paper, non-negative matrix factorization (NMF) is used to separate speech and music signals based on a single channel recording. The assumption that if two independent zero-mean signals are added then their energies are also added has led us to develop a two-stage method (training and separation) that works on time-frequency domain. The performance of the method in separation is evaluated...
The lack of a flexible analysis model has been introduced as an important issue in different applications like source separation. In this paper, a fixed dimension modified sinusoid model (FD-MSM) is proposed for analysis of all audible signals consisting of speech, music and their mixtures. Employing the peak picking in Meldomain gives rise to a fixed number of parameters in the proposed FDMSM, which...
The underdetermined blind source separation problem is the one of separating a large number of sources using a smaller number of mixtures. The DUET algorithm presents a solution to the underdetermined blind source separation problem, demixing an arbitrary number of speech signals from only 2 anechoic mixtures of them. Here the DUET method is presented as a general framework for extending classic array...
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