Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
A unifying review of linear Gaussian models
Neural Computation
Mean-field approaches to independent component analysis
Neural Computation
State-Space Models: From the EM Algorithm to a Gradient Approach
Neural Computation
Multichannel signal separation: methods and analysis
IEEE Transactions on Signal Processing
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
ACM Transactions on Accessible Computing (TACCESS)
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We apply a type of generative modelling to the problem of blind source separation in which prior knowledge about the latent source signals, such as time-varying auto-correlation and quasi-periodicity, are incorporated into a linear state-space model. In simulations, we show that in terms of signal-to-error ratio, the sources are inferred more accurately as a result of the inclusion of strong prior knowledge. We explore different schemes of maximum-likelihood optimization for the purpose of learning the model parameters. The Expectation Maximization algorithm, which is often considered the standard optimization method in this context, results in slow convergence when the noise variance is small. In such scenarios, quasi-Newton optimization yields substantial improvements in a range of signal to noise ratios. We analyze the performance of the methods on convolutive mixtures of speech signals.