• Corpus ID: 8241760

Handbook of Blind Source Separation: Independent Component Analysis and Applications

  title={Handbook of Blind Source Separation: Independent Component Analysis and Applications},
  author={Pierre Comon and Christian Jutten},
Edited by the people who were forerunners in creating the field, together with contributions from 34 leading international experts, this handbook provides the definitive reference on Blind Source Separation, giving a broad and comprehensive description of all the core principles and methods, numerical algorithms and major applications in the fields of telecommunications, biomedical engineering and audio, acoustic and speech processing. Going beyond a machine learning perspective, the book… 
Fuzzy Clustering of Independent Components within Time-Domain Blind Audio Source Separation Method
In this paper, several novel criteria that are suitable to measure the similarity between audio components are proposed and compared by experiments, and conclusions are drawn.
Sensitivity analysis of blind separation of speech mixtures
A method that can track variations in the mixing system by seeking a compromise between adaptive and block methods by using mini-batches and a robust method based on low-order non-integer moments by exploiting the Laplacian model of speech signals.
Blind source separation problem algorithms for audio and biomedical signals
The results suggest that fastICA algorithm based on negentropy is performed better in most cases, however, in EEG signals, it is not possible to conclude the same due to the lack of ground truth, therefore, the choice of better ICA algorithm to separate EEG signals it is still an open problem in the biomedical applications.
A Survey of Kurtosis Optimization Schemes for MISO Source Separation and Equalization
The present chapter reviews some of the iterative algorithms most widely used for MISO source separation and equalization based on kurtosis, including gradient and Newton search methods, algorithms with optimal step-size selection, as well as techniques based on reference signals.
Blind Source Separation of Speech Signals using Mixing Matrix Estimation and Subspace Method
This paper provides subspace projection method with enhanced functionality, which can be used for Underdetermined Blind Source Separation (UBSS) and shows that the proposed method overcomes the shortage of conventional subspace method and achieves higher separation performance.
Guided Matching Pursuit and its Application to Sound Source Separation
This work proposes a new MP variant termed guided matching pursuit (GMP), an iterative algorithm that decomposes a signal into a set of elementary waveforms called atoms chosen from an over-complete dictionary in such a way so that they represent the inherent signal structures.
Application of multi-objective optimization to blind source separation
A RobustICA-based algorithmic system for blind separation of convolutive mixtures
A frequency-domain method employing robust independent component analysis (RICA) to address the multichannel Blind Source Separation (BSS) problem of convolutive speech mixtures in highly reverberant environments and demonstrates the superiority of the presented convolutive algorithmic system in comparison to other BSS algorithms.
A Unified Probabilistic View on Spatially Informed Source Separation and Extraction Based on Independent Vector Analysis
This paper addresses the problem of blind source separation by incorporating prior knowledge into the adaptation of the demixing filters, e.g., the position of the sources, in a probabilistic framework based on Independent Vector Analysis (IVA) as it elegantly and successfully deals with the internal permutation problem.


A new simple source separation method which uses time-frequency information to cancel one source signal from two observations in linear instantaneous mixtures and performs source cancellation when the two considered mixtures contain more than two sources is proposed.
We present a real-time version of the DUET algorithm for the blind separation of any number of sources using only two mixtures. The method applies when sources are Wdisjoint orthogonal, that is, when
Blind source separation by sparse decomposition
This work exploits the property of the sources to have a sparse representation in a corresponding signal dictionary, which provides faster and more robust computations, when there are an equal number of sources and mixtures.
Statistical properties of STFT ratios for two channel systems and applications to blind source separation
This paper analyzes and extends a source separation method based on the use of STFT ratios of two sensor inputs, called DUET, and proves that considerably weaker assumptions about the classes of input signals are required to apply the derived techniques.
A Multiscale Framework For Blind Separation of Linearly Mixed Signals
Take advantage of the properties of multiscale transforms, such as wavelet packets, to decompose signals into sets of local features with various degrees of sparsity, and study how the separation error is affected by the sparsity of decomposition coefficients.
Robust independent component analysis for blind source separation and extraction with application in electrocardiography
  • V. ZarzosoP. Comon
  • Computer Science
    2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
  • 2008
It is explained how RobustICA can easily be modified to target particular sources according to their impulsive character as measured by the kurtosis sign, which makes it possible to extract the sources of interest only, or a subspace thereof, with the subsequent reduction in computational complexity and error accumulation.
Blind separation methods based on Pearson system and its extensions
Blind Source Separation by Sparse Decomposition in a Signal Dictionary
This work suggests a two-stage separation process: a priori selection of a possibly overcomplete signal dictionary in which the sources are assumed to be sparsely representable, followed by unmixing the sources by exploiting the their sparse representability.
Blind separation of dependent sources using the "time-frequency ratio of mixtures" approach
  • F. AbrardY. Deville
  • Computer Science
    Seventh International Symposium on Signal Processing and Its Applications, 2003. Proceedings.
  • 2003
The principles of the TIFROM approach are recalled and it is shown that, unlike independent component analysis methods, this approach can separate dependent signals, provided there exist some areas in the time-frequency plane where only one source occurs.