We recently introduced two algorithms for sparse non-negative matrix
factor 2-D deconvolution (SNMF2D) that are
useful for single channel source separation and music transcription. We here extend this approach to the analysis of the log-frequency spectrograms of a
multichannel recording. The model proposed forms a non-negative
tensor factor 2-D deconvolution (NTF2D) based on the parallel factor
(PARAFAC) model. Two algorithms are given for NTF2D; one based on
least squares the other on Kullback-Leibler divergence minimization.
Both algorithms are extended to give sparse decompositions. The
algorithms are demonstrated to successfully identify the components
of both artificially generated as well as real stereo music.
Publication status | Published - 2006 |
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