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Solution of permutation problem in frequency domain ICA, using multivariate probability density functions. (English) Zbl 1178.94075
Rosca, Justinian (ed.) et al., Independent component analysis and blind signal separation. 6th international conference, ICA 2006, Charleston, SC, USA, March 5–8, 2006. Proceedings. Berlin: Springer (ISBN 3-540-32630-8/pbk). Lecture Notes in Computer Science 3889, 601-608 (2006).
Summary: Conventional Independent Component Analysis (ICA) in frequency domain inherently causes the permutation problem. To solve the problem fundamentally, we propose a new framework for separation of the whole spectrograms instead of the conventional binwise separation. Under our framework, a measure of independence is calculated from the whole spectrograms, not individual frequency bins. For the calculation, we introduce some multivariate probability density functions (PDFs) which take a spectrum as arguments. To seek the unmixing matrix that makes spectrograms independent, we demonstrate a gradient-based algorithm using multivariate activation functions derived from the PDFs. Through experiments using real sound data, we have confirmed that our framework is effective to generate permutation-free unmixed results.
For the entire collection see [Zbl 1096.94002].

MSC:
94A12 Signal theory (characterization, reconstruction, filtering, etc.)
68T05 Learning and adaptive systems in artificial intelligence
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