Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
A fast fixed-point algorithm for independent component analysis
Neural Computation
Independent component analysis: theory and applications
Independent component analysis: theory and applications
High-order contrasts for independent component analysis
Neural Computation
Neural Computation
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Kernel independent component analysis
The Journal of Machine Learning Research
On the Slow Convergence of EM and VBEM in Low-Noise Linear Models
Neural Computation
Topographic Independent Component Analysis
Neural Computation
Blind separation of delayed sources based on information maximization
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 06
Evaluation of blind signal separation method using directivity pattern under reverberant conditions
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 05
Blind Source Separation Exploiting Higher-Order Frequency Dependencies
IEEE Transactions on Audio, Speech, and Language Processing
On the Assumption of Spherical Symmetry and Sparseness for the Frequency-Domain Speech Model
IEEE Transactions on Audio, Speech, and Language Processing
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
Independent component analysis based on nonparametric density estimation
IEEE Transactions on Neural Networks
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
Stability of independent vector analysis
Signal Processing
A matrix joint diagonalization approach for complex independent vector analysis
LVA/ICA'12 Proceedings of the 10th international conference on Latent Variable Analysis and Signal Separation
A parallel dual matrix method for blind signal separation
Neural Computation
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Convolutive mixtures of signals, which are common in acoustic environments, can be difficult to separate into their component sources. Here we present a uniform probabilistic framework to separate convolutive mixtures of acoustic signals using independent vector analysis (IVA), which is based on a joint distribution for the frequency components originating from the same source and is capable of preventing permutation disorder. Different gaussian mixture models (GMM) served as source priors, in contrast to the original IVA model, where all sources were modeled by identical multivariate Laplacian distributions. This flexible source prior enabled the IVA model to separate different type of signals. Three classes of models were derived and tested: noiseless IVA, online IVA, and noisy IVA. In the IVA model without sensor noise, the unmixing matrices were efficiently estimated by the expectation maximization (EM) algorithm. An online EM algorithm was derived for the online IVA algorithm to track the movement of the sources and separate them under nonstationary conditions. The noisy IVA model included the sensor noise and combined denoising with separation. An EM algorithm was developed that found the model parameters and separated the sources simultaneously. These algorithms were applied to separate mixtures of speech and music. Performance as measured by the signal-to-interference ratio (SIR) was substantial for all three models.