Shifted Independent Component Analysis  Morten Mørup, Kristoffer Hougaard Madsen, Lars Kai Hansen
 Abstract  Delayed mixing is a problem of theoretical interest and practical importance, e.g., in speech processing, biomedical 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 timedomain representation. Here, we explore the fact that a shift t in the time domain corresponds to a multiplication of exp(iwt) in 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 prune to local minima and difficulties arise especially in the presence of large delays and high frequency sources. A Matlab implementation can be downloaded from www2.imm.dtu.dk/pubdb/views/publication\_details.php? id=5206.  Keywords  Source separation, unsupervised learning, convolutive models  Type  Conference paper [With referee]  Conference  ICA2007  Year  2007 pp. 8996  BibTeX data  [bibtex]  IMM Group(s)  Intelligent Signal Processing 
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