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322
Survey of Sparse and NonSparse Methods in Source Separation
, 2005
"... Source separation arises in a variety of signal processing applications, ranging from speech processing to medical image analysis. The separation of a superposition of multiple signals is accomplished by taking into account the structure of the mixing process and by making assumptions about the sour ..."
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Cited by 51 (1 self)
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Source separation arises in a variety of signal processing applications, ranging from speech processing to medical image analysis. The separation of a superposition of multiple signals is accomplished by taking into account the structure of the mixing process and by making assumptions about the sources. When the information about the mixing process and sources is limited, the problem is called ‘blind’. By assuming that the sources can be represented sparsely in a given basis, recent research has demonstrated that solutions to previously problematic blind source separation problems can be obtained. In some cases, solutions are possible to problems intractable by previous nonsparse methods. Indeed, sparse methods provide a powerful approach to the separation of linear mixtures of independent data. This paper surveys the recent arrival of sparse blind source separation methods and the previously existing nonsparse methods, providing insights and appropriate hooks into the literature along the way.
The Cocktail Party Problem
, 2005
"... This review presents an overview of a challenging problem in auditory perception, the cocktail party phenomenon, the delineation of which goes back to a classic paper by Cherry in 1953. In this review, we address the following issues: (1) human auditory scene analysis, which is a general process car ..."
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Cited by 47 (0 self)
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This review presents an overview of a challenging problem in auditory perception, the cocktail party phenomenon, the delineation of which goes back to a classic paper by Cherry in 1953. In this review, we address the following issues: (1) human auditory scene analysis, which is a general process carried out by the auditory system of a human listener; (2) insight into auditory perception, which is derived from Marr’s vision theory; (3) computational auditory scene analysis, which focuses on specific approaches aimed at solving the machine cocktail party problem; (4) active audition, the proposal for which is motivated by analogy with active vision, and (5) discussion of brain theory and independent component analysis, on the one hand, and correlative neural firing, on the other.
A general flexible framework for the handling of prior information in audio source separation
 IEEE Transactions on Audio, Speech and Signal Processing
, 2012
"... Abstract—Most of audio source separation methods are developed for a particular scenario characterized by the number of sources and channels and the characteristics of the sources and the mixing process. In this paper we introduce a general audio source separation framework based on a library of str ..."
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Cited by 45 (17 self)
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Abstract—Most of audio source separation methods are developed for a particular scenario characterized by the number of sources and channels and the characteristics of the sources and the mixing process. In this paper we introduce a general audio source separation framework based on a library of structured source models that enable the incorporation of prior knowledge about each source via userspecifiable constraints. While this framework generalizes several existing audio source separation methods, it also allows to imagine and implement new efficient methods that were not yet reported in the literature. We first introduce the framework by describing the model structure and constraints, explaining its generality, and summarizing its algorithmic implementation using a generalized expectationmaximization algorithm. Finally, we illustrate the abovementioned capabilities of the framework by applying it in several new and existing configurations to different source separation problems. We have released a software tool named Flexible Audio Source Separation Toolbox (FASST) implementing a baseline version of the framework in Matlab. Index Terms—Audio source separation, local Gaussian model, nonnegative matrix factorization, expectationmaximization I.
Oracle estimators for the benchmarking of source separation algorithms
 Queen Mary, University of London
, 2006
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Modelbased expectationmaximization source separation and localization,”
 IEEE Trans. Audio, Speech, and Language Process. (ASLP),
, 2010
"... ..."
A robust method to count and locate audio sources in a stereophonic linear instantaneous mixture
 in ICA
, 2006
"... Abstract—We propose a method to count and estimate the mixing directions in an underdetermined multichannel mixture. The approach is based on the hypothesis that in the neighbourhood of some timefrequency points, only one source essentially contributes to the mixture: such timefrequency points can ..."
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Cited by 41 (10 self)
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Abstract—We propose a method to count and estimate the mixing directions in an underdetermined multichannel mixture. The approach is based on the hypothesis that in the neighbourhood of some timefrequency points, only one source essentially contributes to the mixture: such timefrequency points can provide robust local estimates of the corresponding source direction. At the core of our contribution is a statistical model to exploit a local confidence measure which detects the timefrequency regions where such robust information is available. A clustering algorithm called DEMIX is proposed to merge the information from all timefrequency regions according to their confidence level. So as to estimate the delays of anechoic mixtures and overcome the intrinsic ambiguities of phase unwrapping as met with DUET, we propose a technique similar to GCCPHAT which is able to estimate delays that can largely exceed one sample. We propose an extensive experimental study which shows that the resulting method is more robust in conditions where all DUETlike comparable methods fail, that is in particular: a) when timedelays largely exceed one sample; b) when the source directions are very close. Index Terms—Blind source separation, multichannel audio, delay estimation, sparse component analysis, direction of arrival I.
Underdetermined blind source separation based on sparse representation
 IEEE Transactions on Signal Processing
, 2006
"... Abstract—This paper discusses underdetermined (i.e., with more sources than sensors) blind source separation (BSS) using a twostage sparse representation approach. The first challenging task of this approach is to estimate precisely the unknown mixing matrix. In this paper, an algorithm for estimat ..."
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Cited by 41 (11 self)
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Abstract—This paper discusses underdetermined (i.e., with more sources than sensors) blind source separation (BSS) using a twostage sparse representation approach. The first challenging task of this approach is to estimate precisely the unknown mixing matrix. In this paper, an algorithm for estimating the mixing matrix that can be viewed as an extension of the DUET and the TIFROM methods is first developed. Standard clustering algorithms (e.g., Kmeans method) also can be used for estimating the mixing matrix if the sources are sufficiently sparse. Compared with the DUET, the TIFROM methods, and standard clustering algorithms, with the authors ’ proposed method, a broader class of problems can be solved, because the required key condition on sparsity of the sources can be considerably relaxed. The second task of the twostage approach is to estimate the source matrix using a standard linear programming algorithm. Another main contribution of the work described in this paper is the development of a recoverability analysis. After extending the results in [7], a necessary and sufficient condition for recoverability of a source vector is obtained. Based on this condition and various types of source sparsity, several probability inequalities and probability estimates for the recoverability issue are established. Finally, simulation results that illustrate the effectiveness of the theoretical results are presented. Index Terms—Blind source separation (BSS), Inorm, probability, recoverability, sparse representation, wavelet packets. I.
First stereo audio source separation evaluation campaign: data, algorithms and results,”
 Proc. ICA,
, 2007
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A SURVEY OF CONVOLUTIVE BLIND SOURCE SEPARATION METHODS
 SPRINGER HANDBOOK ON SPEECH PROCESSING AND SPEECH COMMUNICATION
"... In this chapter, we provide an overview of existing algorithms for blind source separation of convolutive audio mixtures. We provide a taxonomy, wherein many of the existing algorithms can be organized, and we present published results from those algorithms that have been applied to realworld audio ..."
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Cited by 39 (0 self)
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In this chapter, we provide an overview of existing algorithms for blind source separation of convolutive audio mixtures. We provide a taxonomy, wherein many of the existing algorithms can be organized, and we present published results from those algorithms that have been applied to realworld audio separation tasks.
Sound Source Separation in Monaural Music Signals
, 2006
"... Sound source separation refers to the task of estimating the signals produced by individual sound sources from a complex acoustic mixture. It has several applications, since monophonic signals can be processed more efficiently and flexibly than polyphonic mixtures. This thesis deals with the separat ..."
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Cited by 36 (4 self)
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Sound source separation refers to the task of estimating the signals produced by individual sound sources from a complex acoustic mixture. It has several applications, since monophonic signals can be processed more efficiently and flexibly than polyphonic mixtures. This thesis deals with the separation of monaural, or, onechannel music recordings. We concentrate on separation methods, where the sources to be separated are not known beforehand. Instead, the separation is enabled by utilizing the common properties of realworld sound sources, which are their continuity, sparseness, and repetition in time and frequency, and their harmonic spectral structures. One of the separation approaches taken here use unsupervised learning and the other uses modelbased inference based on sinusoidal modeling. Most of the existing unsupervised separation algorithms are based on a linear instantaneous signal model, where each frame of the input mixture signal is