An approach to blind source separation based on temporal structure of speech signals

By Noboru Murata, Shiro Ikeda, Andreas Ziehe. Neurocomputing, 2001

618 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Signal Processing

View Paper: https://www.semanticscholar.org/paper/7e745ef5cb1e19c129ccb7994f51f10dd2e3645d

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In Analytical Chemistry

In Signal Processing

Citations in the corpus

A robust and precise method for solving the permutation problem of frequency-domain blind source separation (2004, methodology): “Once we estimate the directions Θ(f) = [θ1(f), . . . , θP (f)]T of target signals extracted by every row of W(f), we can align the permutations according to Θ(f).”
Determined Blind Source Separation Unifying Independent Vector Analysis and Nonnegative Matrix Factorization (2016, methodology): “The signal scale can be restored by applying a back-projection technique [13] after the optimization.”
Blind Source Separation Exploiting Higher-Order Frequency Dependencies (2007, background): “correlations of signal envelopes to align permutations [15], [16].”
Performance measurement in blind audio source separation (2006, methodology): “We test two methods to address this problem: the method outlined in [5] and an oracle (i.”
Blind Source Separation and Independent Component Analysis: A Review (2005, methodology): “One of the algorithms to solve the permutation and scaling problem makes use of the envelopes of frequency spectra assuming that the independent components have time-varying statistical properties [111, 9].”
Independent Vector Analysis: An Extension of ICA to Multivariate Components (2006, methodology): “method that limits the filter length in the time domain [4], uses direction of arrival estimation [5], and uses inter-frequency correlation [ 6 ].”
Stable and fast update rules for independent vector analysis based on auxiliary function technique (2011, neutral): “(37)”
Underdetermined Convolutive Blind Source Separation via Frequency Bin-Wise Clustering and Permutation Alignment (2011, methodology): “Amplitude envelopes are widely used [9], [10], [24], [25] to represent the activity of separated signals and thus for permutation alignment.”
Blind Source Separation and Independent Component Analysis : A Review (2004, methodology): “One of the algorithms to solve the permutation and scaling problem makes use of the envelopes of frequency spectra assuming that the independent components have time-varying statistical properties [111, 9].”