β-divergence nonnegative tensor factorization with pre-trained spectral bases yields better audio source separation performance than comparable algorithms across multiple reverberant mixing conditions.
Audio Source Sepa- ration using a Redundant Library of Source Spectral Bases for Non-negative Tensor Factorization,
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Audio Source Separation in Reverberant Environments using $\beta$-divergence based Nonnegative Factorization
β-divergence nonnegative tensor factorization with pre-trained spectral bases yields better audio source separation performance than comparable algorithms across multiple reverberant mixing conditions.