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Nonnegative matrix factorization (NMF) is an effective speech separation approach of extracting discriminative components of different speaker. However, traditional NMF focuses only on the additive combination of the components and ignores the dependencies of speeches. Convolutive NMF (CNMF) captures the dependencies of speeches by overlapping components and achieves better separation performance...
In this paper, to address problems in multichannel music signal separation, we propose a new hybrid method that combines directional clustering and advanced nonnegative matrix factorization (NMF). The aims of multichannel music signal separation technology is to extract a specific target signal from observed multichannel signals that contain multiple instrumental sounds. In previous studies, various...
A single channel speech-music separation algorithm based on nonnegative matrix factorization (NMF) with spectral masks is proposed in this work. The proposed algorithm uses training data of speech and music signals with nonnegative matrix factorization followed by masking to separate the mixed signal. In the training stage, NMF uses the training data to train a set of basis vectors for each source...
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