Nonnegative matrix factorization with quadratic programming
Neurocomputing
Nonnegative matrix factorization with Gaussian process priors
Computational Intelligence and Neuroscience - Advances in Nonnegative Matrix and Tensor Factorization
Computational Intelligence and Neuroscience - Advances in Nonnegative Matrix and Tensor Factorization
IEEE Transactions on Signal Processing
On connection between the convolutive and ordinary nonnegative matrix factorizations
LVA/ICA'12 Proceedings of the 10th international conference on Latent Variable Analysis and Signal Separation
Separation of human and animal seismic signatures using non-negative matrix factorization
Pattern Recognition Letters
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We present a novel method for blind separation of instruments in single channel polyphonic music based on a non-negative matrix factor 2-D deconvolution algorithm. The method is an extention of NMFD recently introduced by Smaragdis [1]. Using a model which is convolutive in both time and frequency we factorize a spectrogram representation of music into components corresponding to individual instruments. Based on this factorization we separate the instruments using spectrogram masking. The proposed algorithm has applications in computational auditory scene analysis, music information retrieval, and automatic music transcription.