Probabilistic Formulation of Independent Vector Analysis Using Complex Gaussian Scale Mixtures
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
Permutation Correction in Blind Source Separation Using Sliding Subband Likelihood Function
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
Joint blind source separation by multiset canonical correlation analysis
IEEE Transactions on Signal Processing
Modeling and estimation of dependent subspaces with non-radially symmetric and skewed densities
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
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
Multivariate analysis of fMRI group data using independent vector analysis
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Real-time independent vector analysis for convolutive blind source separation
IEEE Transactions on Circuits and Systems Part I: Regular Papers
Glimpsing IVA: a framework for overcomplete/complete/undercomplete convolutive source separation
IEEE Transactions on Audio, Speech, and Language Processing - Special issue on processing reverberant speech: methodologies and applications
Correlation-based amplitude estimation of coincident partials in monaural musical signals
EURASIP Journal on Audio, Speech, and Music Processing
Stability analysis on independent vector analysis
ROCOM'11/MUSP'11 Proceedings of the 11th WSEAS international conference on robotics, control and manufacturing technology, and 11th WSEAS international conference on Multimedia systems & signal processing
Multivariate scale mixture of gaussians modeling
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Stability of independent vector analysis
Signal Processing
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
An audio-video based IVA algorithm for source separation and evaluation on the AV16.3 corpus
LVA/ICA'12 Proceedings of the 10th international conference on Latent Variable Analysis and Signal Separation
Group Study of Simulated Driving fMRI Data by Multiset Canonical Correlation Analysis
Journal of Signal Processing Systems
Jacobi iterations for Canonical Dependence Analysis
Signal Processing
Digital Signal Processing
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Blind source separation (BSS) is a challenging problem in real-world environments where sources are time delayed and convolved. The problem becomes more difficult in very reverberant conditions, with an increasing number of sources, and geometric configurations of the sources such that finding directionality is not sufficient for source separation. In this paper, we propose a new algorithm that exploits higher order frequency dependencies of source signals in order to separate them when they are mixed. In the frequency domain, this formulation assumes that dependencies exist between frequency bins instead of defining independence for each frequency bin. In this manner, we can avoid the well-known frequency permutation problem. To derive the learning algorithm, we define a cost function, which is an extension of mutual information between multivariate random variables. By introducing a source prior that models the inherent frequency dependencies, we obtain a simple form of a multivariate score function. In experiments, we generate simulated data with various kinds of sources in various environments. We evaluate the performances and compare it with other well-known algorithms. The results show the proposed algorithm outperforms the others in most cases. The algorithm is also able to accurately recover six sources with six microphones. In this case, we can obtain about 16-dB signal-to-interference ratio (SIR) improvement. Similar performance is observed in real conference room recordings with three human speakers reading sentences and one loudspeaker playing music