Open AccessJournal Article
Polar coordinate based nonlinear function for frequency-domain blind source separation
TLDR
In this article, a new nonlinear function for independent component analysis to process complex-valued signals, which is used in frequency-domain blind source separation, is presented. But the difference between the two types of functions is in the assumed densities of independent components.Abstract:
This paper presents a new type of nonlinear function for independent component analysis to process complex-valued signals, which is used in frequency-domain blind source separation. The new function is based on the polar coordinates of a complex number, whereas the conventional one is based on the Cartesian coordinates. The new function is derived from the probability density function of frequency-domain signals that are assumed to be independent of the phase. We show that the difference between the two types of functions is in the assumed densities of independent components. Experimental results for separating speech signals show that the new nonlinear function behaves better than the conventional one.read more
Citations
More filters
Journal ArticleDOI
A robust and precise method for solving the permutation problem of frequency-domain blind source separation
TL;DR: By utilizing the harmonics of signals, the new method is robust even for low frequencies where DOA estimation is inaccurate, and provides an almost perfect solution to the permutation problem for a case where two sources were mixed in a room whose reverberation time was 300 ms.
Journal ArticleDOI
A Consolidated Perspective on Multimicrophone Speech Enhancement and Source Separation
TL;DR: This paper proposes to analyze a large number of established and recent techniques according to four transverse axes: 1) the acoustic impulse response model, 2) the spatial filter design criterion, 3) the parameter estimation algorithm, and 4) optional postfiltering.
A survey of convolutive blind source separation methods
TL;DR: A taxonomy is provided, wherein many of the existing algorithms for blind source separation of convolutive audio mixtures can be organized, and results from those algorithms that have been applied to real-world audio separation tasks are presented.
Journal ArticleDOI
Generalization Characteristics of Complex-Valued Feedforward Neural Networks in Relation to Signal Coherence
Akira Hirose,Shotaro Yoshida +1 more
TL;DR: Simulation and real-world experiments demonstrate that CVNNs with amplitude-phase-type activation function show smaller generalization error than real-valued networks, such as bivariate and dual-univariate real- valued neural networks.
Journal ArticleDOI
Blind source separation based on a fast-convergence algorithm combining ICA and beamforming
TL;DR: The signal separation performance of the proposed algorithm is superior to that of the conventional ICA-based BSS method, even under reverberant conditions, and the temporal alternation between ICA and beamforming can realize fast- and high-convergence optimization.
References
More filters
Journal ArticleDOI
An information-maximization approach to blind separation and blind deconvolution
TL;DR: It is suggested that information maximization provides a unifying framework for problems in "blind" signal processing and dependencies of information transfer on time delays are derived.
Journal ArticleDOI
Independent component analysis, a new concept?
TL;DR: An efficient algorithm is proposed, which allows the computation of the ICA of a data matrix within a polynomial time and may actually be seen as an extension of the principal component analysis (PCA).
Journal ArticleDOI
Convolutive blind separation of non-stationary sources
Lucas C. Parra,Clay D. Spence +1 more
TL;DR: This work tackles the problem of source separation by explicitly exploiting the nonstationarity of the acoustic sources, and finds an FIR backward model, which generates well separated model sources.
Journal ArticleDOI
Blind separation of convolved mixtures in the frequency domain
TL;DR: It is observed that convolved Mixing in the time domain corresponds to instantaneous mixing in the frequency domain, and convolved mixing can be inverted using simpler and more robust algorithms than the ones recently developed.
Book
Independent Component Analysis: Theory and Applications
TL;DR: This work presents a Unifying Information-Theoretic Framework for ICA, a novel and scalable framework for independent component analysis that combines supervised and unsupervised classification with ICA Mixture Models.