DEMIXING JAZZ-MUSIC: SPARSE CODING NEURAL GAS FOR THE SEPARATION OF NOISY OVERCOMPLETE SOURCES
We consider the problem of separating noisy overcomplete, sources from linear mixtures, i.e., we observe N mixtures of M > N sparse sources. We, show that the "Sparse Coding Neural Gas" (SCNG) algorithm [8, 9] call be employed in order to estimate the mixing matrix. Based oil the learned mixing matrix the sources are obtained by orthogonal matching pursuit. Using synthetically generated data, we evaluate the influence of (i) the coherence of the mixing matrix, (ii) the noise level, and (iii) the sparseness of the sources with respect, to the performance that can be achieved oil the representation level. Our results show that if the coherence of the mixing matrix and the noise level are sufficiently small and the underlying sources are sufficiently sparse, the sources can be estimated from the observed mixtures. In order to apply our method to real-world data, we try to C, jazz audio signal given only a two-channel reconstruct each single instrument of a recording. Furthermore. we compare our method to the well-known FastICA [4] algorithm and show that, in case of sparse sources and presence of additive noise, our method provides a superior estimation of the mixing matrix.
sparse coding neural gas blind source separation self-organizing maps unsupervised learning matching prusuit