We propose an unsupervised inference procedure for audio source separation. Components in nonnegative matrix factorization (NMF) are grouped automatically in audio sources via a p...
In this paper we are interested in non-negative matrix factorization (NMF) with the Itakura-Saito (IS) divergence. Previous work has demonstrated the relevance of this cost functi...
This paper concerns the adaptation of spectrum dictionaries in audio source separation with supervised learning. Supposing that samples of the audio sources to separate are availa...
Xabier Jaureguiberry, Pierre Leveau, Simon Maller,...
We consider inference in a general data-driven object-based model of multichannel audio data, assumed generated as a possibly underdetermined convolutive mixture of source signals...
—We present in this paper a general formulation for nonnegative data factorization, called projective nonnegative graph embedding (PNGE), which 1) explicitly decomposes the data ...