Non-negative Matrix Factorization with Sparseness Constraints
The Journal of Machine Learning Research
Csiszár’s divergences for non-negative matrix factorization: family of new algorithms
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Nonnegative matrix factor 2-d deconvolution for blind single channel source separation
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
IEEE Transactions on Audio, Speech, and Language Processing
Convolutive Speech Bases and Their Application to Supervised Speech Separation
IEEE Transactions on Audio, Speech, and Language Processing
Single channel music sound separation based on spectrogram decomposition and note classification
CMMR'10 Proceedings of the 7th international conference on Exploring music contents
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
Journal of Signal Processing Systems
Computer Speech and Language
Hi-index | 35.68 |
Using the convolutive nonnegative matrix factorization (NMF) model due to Smaragdis, we develop a novel algorithm for matrix decomposition based on the squared Euclidean distance criterion. The algorithm features new formally derived learning rules and an efficient update for the reconstructed nonnegative matrix. Performance comparisons in terms of computational load and audio onset detection accuracy indicate the advantage of the Euclidean distance criterion over the Kullback-Leibler divergence criterion.