Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Estimation of time delays between unknown colored signals
Signal Processing
Complex independent component analysis of frequency-domain electroencephalographic data
Neural Networks - Special issue: Neuroinformatics
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
Anechoic Blind Source Separation Using Wigner Marginals
The Journal of Machine Learning Research
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Delayed mixing is a problem of theoretical interest and practical importance, e.g., in speech processing, bio-medical signal analysis and financial data modelling. Most previous analyses have been based on models with integer shifts, i.e., shifts by a number of samples, and have often been carried out using time-domain representation. Here, we explore the fact that a shift τ in the time domain corresponds to a multiplication of e-iωτin the frequency domain. Using this property an algorithm in the case of sources≤sensors allowing arbitrary mixing and delays is developed. The algorithm is based on the following steps: 1) Find a subspace of shifted sources. 2) Resolve shift and rotation ambiguity by information maximization in the complex domain. The algorithm is proven to correctly identify the components of synthetic data. However, the problem is prone to local minima and difficulties arise especially in the presence of large delays and high frequency sources. A Matlab implementation can be downloaded from [1].