M. Davies’s research while affiliated with Queen Mary University of London and other places

The problem of source separation of instantaneous mixtures has been addressed thoroughly in literature in the past. The assumption of statistical independence between the source signals, led to the introduction of independent component analysis (ICA). A number of methods, based on the ICA framework, can identify nonGaussian sources in instantaneous mixtures with robust convergence and performance. However, in several biomedical applications, there is a need to identify and separate signals that, apart from being nonGaussian, are not symmetric. In this article, the authors present a method for blind identification and separation of skewed (non-symmetric) signals in a linear instantaneous mixture.

The use of mixture of Gaussians (MoGs) for noisy and overcomplete independent component analysis (ICA) when the source distributions are very sparse is explored. The sparsity model can often be justified if an appropriate transform, such as the modified discrete cosine transform, is used. Given the sparsity assumption, a number of simplifying approximations are introduced to the observation density that avoid the exponential growth of mixture components. An efficient clustering algorithm is derived whose complexity grows linearly with the number of sources and it is shown that it is capable of performing reasonable separation.

We examine the problem of blind audio source separation using independent component analysis (ICA). In order to separate audio sources recorded in a real recording environment, we need to model the mixing process as convolutional. Many methods have been introduced for separating convolved mixtures, the most successful of which require working in the frequency domain. This paper proposes a fixed-point algorithm for performing fast frequency domain ICA, as well as a method to increase the stability and enhance the performance of previous frequency domain ICA algorithms

One of the most powerful techniques applied to blind audio source separation is Independent Component Analysis (ICA). For the separation of audio sources recorded in a real environment, we need to model the mixing process as convolutional. Many methods have been introduced for separating convolved mixtures, the most successful of which require working in the frequency domain (1), (2), (3), (4). Most of these methods perform efficient separation of convolved mixtures, however they are relatively slow. The authors propose two fixed-point algorithms for performing fast frequency domain ICA.