![]() ![]() Popular music is often composed of an accompaniment and a lead component, the latter typically consisting of vocals. The optimal segmentation shows an improvement for parameter estimation success rate, compared to the uniform segmentation. Simulations on synthetic and real audio shows promising results for source parameter estimation and number of sources estimated across segments. ![]() The generalized variance and degree of membership of the Gaussian components across segments is used as a basis for the proposed selection of clusters amongst candidates. For each segment parameters are estimated using a minimum description length algorithm for mixtures based on the expectation-maximization algorithm. The parameter distribution, for both individual segments and across segments that comprise the entire signal, is modelled as a Gaussian mixture. The method is based on clustering of narrowband interaural level and time differences for an unknown number of sources and uses an optimal segmentation on which the clustering is based. In this paper, we propose a method for finding the number of sources and their parameters from stereophonic mixtures.
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