A Uniform Framework for Ad-Hoc Indexes to Answer Reachability Queries on Large Graphs
DASFAA '09 Proceedings of the 14th International Conference on Database Systems for Advanced Applications
Multichannel nonnegative matrix factorization in convolutive mixtures for audio source separation
IEEE Transactions on Audio, Speech, and Language Processing
A robust method to count and locate audio sources in a multichannel underdetermined mixture
IEEE Transactions on Signal Processing
Audio source separation using hierarchical phase-invariant models
NOLISP'09 Proceedings of the 2009 international conference on Advances in Nonlinear Speech 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
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We consider inference in a general data-driven object-based model of multichannel audio data, assumed generated as a possibly under-determined convolutive mixture of source signals. Each source is given a model inspired from nonnegative matrix factorization (NMF) with the Itakura-Saito divergence, which underlies a statistical model of superimposed Gaussian components. We address estimation of the mixing and source parameters using two methods. The first one consists of maximizing the exact joint likelihood of the multichannel data using an expectation-maximization algorithm. The second method consists of maximizing the sum of individual likelihoods of all channels using a multiplicative update algorithm inspired from NMF methodology. Our decomposition algorithms were applied to stereo music and assessed in terms of blind source separation performance.