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In this work, we study the usefulness of several types of sparsity penalties in the task of speech separation using supervised and semi-supervised Nonnegative Matrix Factorization (NMF). We compare different criteria from the literature to two novel penalty functions based on Wiener Entropy, in a large-scale evaluation on spontaneous speech overlaid by realistic domestic noise, as well as music and...
We present a novel method to integrate noise estimates by unsupervised speech enhancement algorithms into a semi-supervised non-negative matrix factorization framework. A multiplicative update algorithm is derived to estimate a non-negative noise dictionary given a time-varying background noise estimate with a stationarity constraint. A large-scale, speaker-independent evaluation is carried out on...
We present a novel and unique combination of algorithms to detect the gender of the leading vocalist in recorded popular music. Building on our previous successful approach that enhanced the harmonic parts by means of Non-Negative Matrix Factorization (NMF) for increased accuracy, we integrate on the one hand a new source separation algorithm specifically tailored to extracting the leading voice from...
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