Blind vector deconvolution: convolutive mixture models in short-time fourier transform domain
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Real-time independent vector analysis for convolutive blind source separation
IEEE Transactions on Circuits and Systems Part I: Regular Papers
Glimpsing IVA: a framework for overcomplete/complete/undercomplete convolutive source separation
IEEE Transactions on Audio, Speech, and Language Processing - Special issue on processing reverberant speech: methodologies and applications
Stability analysis on independent vector analysis
ROCOM'11/MUSP'11 Proceedings of the 11th WSEAS international conference on robotics, control and manufacturing technology, and 11th WSEAS international conference on Multimedia systems & signal processing
Stability of independent vector analysis
Signal Processing
Digital Signal Processing
Infinite sparse factor analysis for blind source separation in reverberant environments
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
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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.