Oracle estimators for the benchmarking of source separation algorithms
Signal Processing
Complex nonconvex lp norm minimization for underdetermined source separation
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
A robust method to count and locate audio sources in a stereophonic linear instantaneous mixture
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Blind separation of speech mixtures via time-frequency masking
IEEE Transactions on Signal Processing
Blind separation of instantaneous mixtures of nonstationary sources
IEEE Transactions on Signal Processing
Blind source separation based on time-frequency signalrepresentations
IEEE Transactions on Signal Processing
Performance measurement in blind audio source separation
IEEE Transactions on Audio, Speech, and Language Processing
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
Blind Spectral-GMM Estimation for Underdetermined Instantaneous Audio Source Separation
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
Under-determined reverberant audio source separation using a full-rank spatial covariance model
IEEE Transactions on Audio, Speech, and Language Processing - Special issue on processing reverberant speech: methodologies and applications
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
Audio source separation using hierarchical phase-invariant models
NOLISP'09 Proceedings of the 2009 international conference on Advances in Nonlinear Speech Processing
| Hi-index | 0.00 |
Underdetermined source separation is often carried out by modeling time-frequency source coefficients via a fixed sparse prior. This approach fails when the number of active sources in one time-frequency bin is larger than the number of channels or when active sources lie on both sides of an inactive source. In this article, we partially address these issues by modeling time-frequency source coefficients via Gaussian priors with free variances. We study the resulting maximum likelihood criterion and derive a fast non-iterative optimization algorithm that finds the global minimum. We show that this algorithm outperforms state-of-the-art approaches over stereo instantaneous speech mixtures.