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|