Abstract
Nonnegative matrix factorization (NMF) is a widely used method for audio source separation. Additional constraints supporting e.g. temporal continuity or sparseness adapt NMF to the structure of audio signals even further. In this paper, we propose generalized NMF constraints which make use of prior information gathered for each component individually. In general, this information could be obtained blindly or by a training step. Here we make use of these novel constraints in an algorithm for informed audio source separation (ISS). ISS uses source separation to code audio objects by assisting a source separation step in the decoder with parameters extracted with knowledge of the sources in the encoder. In [1], a novel algorithm for ISS was proposed which makes use of an NMF step in the decoder. We show in experiments that the generalized constraints enhance the separation quality while keeping the additionally needed bit rate very low.
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