A Robust and Precise Method for Solving the Permutation Problem of Frequency-Domain Blind Source Separation

Sawada, Hiroshi; Mukai, Ryo; Araki, Shoko; Makino, Shoji

doi:10.1109/tsa.2004.832994

Cited by 489 publications

(352 citation statements)

References 30 publications

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“…where x q (n) is the signal recorded at the q-th microphone at time sample n, s p (n) is the p-th source signal, a qp (l) denotes the impulse response of the mixing filter from source p to sensor q, and L m is the maximum length of all impulse responses [13]. The source signals s p are typically assumed to be independent.…”

Section: Problem Statementmentioning

confidence: 99%

An adaptive stereo basis method for convolutive blind audio source separation

et al. 2008

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We consider the problem of convolutive blind source separation of stereo mixtures, where a pair of microphones records mixtures of sound sources that are convolved with the impulse response between each source and sensor. We propose an Adaptive Stereo Basis (ASB) source separation method for such convolutive mixtures, using an adaptive transform basis which is learned from the stereo mixture pair.The stereo basis vector pairs of the transform are grouped according to the estimated relative delay between the left and right channels for each basis, and the sources are then extracted by projecting the transformed signal onto the subspace corresponding to each group of basis vector pairs. The performance of the proposed algorithm is compared with FD-ICA and DUET under different reverberation and noise conditions, using both objective distortion measures and formal listening tests. Preprint submitted to Elsevier Science 10 August 2007NOTICE: this is the author's version of a work that was accepted for publication in Neurocomputing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in : Neurocomputing 71(10-12), 2087-2097, June 2008. doi: 10.1016/j.neucom.2007 The results indicate that the proposed stereo coding method is competitive with both these algorithms at short and intermediate reverberation times, and offers significantly improved performance at low noise and short reverberation times.

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Section: Problem Statementmentioning

confidence: 99%

An adaptive stereo basis method for convolutive blind audio source separation

et al. 2008

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“…Among them, we consider the frequency-domain approach [6][7][8][9][10][11][12][13][14] where we apply a short-time Fourier transform (STFT) to the sensor observations xj(t). If we use a sufficiently long frame for STFT to cover the main part of the impulse responses h jk , the convolutive mixture (1) can be approximated well with an instantaneous mixture at each frequency f :…”

Section: Introductionmentioning

confidence: 99%

“…The ambiguities should be aligned properly so that the separated signals that originate from the same source are grouped together. This problem is known as the permutation problem of frequencydomain BSS, and various methods [6][7][8][9][10][11][12][13][14] have been proposed for its solution. Section 4 discusses a strategy that exploits the mutual dependence of bin-wise separated signals across frequencies [8][9][10]14].…”

Section: Introductionmentioning

confidence: 99%

“…This problem is known as the permutation problem of frequencydomain BSS, and various methods [6][7][8][9][10][11][12][13][14] have been proposed for its solution. Section 4 discusses a strategy that exploits the mutual dependence of bin-wise separated signals across frequencies [8][9][10]14]. The advantage of this strategy is that it is less affected by bad mixing conditions, such as severe reverberations or closely located sources, than another popular strategy based on time-difference-of-arrival estimations [10,11].…”

Section: Introductionmentioning

confidence: 99%

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MLSP 2007 Data Analysis Competition: Frequency-Domain Blind Source Separation for Convolutive Mixtures of Speech/Audio Signals

Sawada

Araki

Makino

2007

2007 IEEE Workshop on Machine Learning for Signal Processing

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This paper describes the frequency-domain approach to the blind source separation of speech/audio signals that are convolutively mixed in a real room environment. With the application of shorttime Fourier transforms, convolutive mixtures in the time domain can be approximated as multiple instantaneous mixtures in the frequency domain. We employ complex-valued independent component analysis (ICA) to separate the mixtures in each frequency bin. Then, the permutation ambiguity of the ICA solutions should be aligned so that the separated signals are constructed properly in the time domain. We propose a permutation alignment method based on clustering the activity sequences of the frequency binwise separated signals. We achieved the overall winner status of MLSP 2007 Data Analysis Competition based on the presented method.

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“…This means that the independent components (ICs) are estimated only up to a multiplicative scalar constant and the order in which the ICs are estimated are not known. In [12] attempts have been made to overcome the permutation ambiguity in the frequency domain only but the ambiguity remains still unresolved in the time domain.…”

Section: Introductionmentioning

confidence: 99%

Constrained genetic algorithm based independent component analysis

Acharya

Panda

Lakshmi

2007

2007 IEEE Congress on Evolutionary Computation

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Abstract-Independent Component Analysis, a computationally efficient statistical signal processing technique, has been an area of interest for researchers for many practical applications in various fields of science and engineering. The present paper proposes a constrained genetic algorithm optimization based independent component analysis assuming a noise free Independent Component Analysis (ICA) model. It investigates on the application and performance of the popular evolutionary computation technique GA in independent component analysis problem. It is observed that the proposed constrained genetic algorithm optimization based ICA overcomes the long standing permutation ambiguity and recovers the independent components in a fixed order which is dependent on the statistical characteristics of the signals to be estimated. The constrained GA based ICA has also been compared with the most popular fast ICA algorithm.

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A Robust and Precise Method for Solving the Permutation Problem of Frequency-Domain Blind Source Separation

Cited by 489 publications

References 30 publications

An adaptive stereo basis method for convolutive blind audio source separation

An adaptive stereo basis method for convolutive blind audio source separation

MLSP 2007 Data Analysis Competition: Frequency-Domain Blind Source Separation for Convolutive Mixtures of Speech/Audio Signals

Constrained genetic algorithm based independent component analysis

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