Non-negative tensor factorization with applications to statistics and computer vision
ICML '05 Proceedings of the 22nd international conference on Machine learning
Bayesian inference for nonnegative matrix factorisation models
Computational Intelligence and Neuroscience
Multichannel nonnegative matrix factorization in convolutive mixtures for audio source separation
IEEE Transactions on Audio, Speech, and Language Processing
First stereo audio source separation evaluation campaign: data, algorithms and results
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Estimating the spatial position of spectral components in audio
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Performance measurement in blind audio source separation
IEEE Transactions on Audio, Speech, and Language Processing
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
Cross Burg entropy maximization and its application to ringing suppression in image reconstruction
IEEE Transactions on Image Processing
Digital Signal Processing
Hi-index | 0.00 |
Nonnegative tensor factorization (NTF) of multichannel spectrograms under PARAFAC structure has recently been proposed by Fitzgerald et al as a mean of performing blind source separation (BSS) of multichannel audio data. In this paper we investigate the statistical source models implied by this approach. We show that it implicitly assumes a nonpoint-source model contrasting with usual BSS assumptions and we clarify the links between the measure of fit chosen for the NTF and the implied statistical distribution of the sources. While the original approach of Fitzgeral et al requires a posterior clustering of the spatial cues to group the NTF components into sources, we discuss means of performing the clustering within the factorization. In the results section we test the impact of the simplifying nonpoint-source assumption on underdetermined linear instantaneous mixtures of musical sources and discuss the limits of the approach for such mixtures.