Blind separation of convolved mixtures in the frequency domain

Smaragdis, Paris

doi:10.1016/s0925-2312(98)00047-2

Cited by 667 publications

(400 citation statements)

References 9 publications

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“…A wide variety of methods have been proposed to address this permutation problem [5][6][7][8]. One interesting approach is to consider the spatial arrangement of the source and microphones: a beamforming approach [17][18][19].…”

Section: Frequency-domain Icamentioning

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: Frequency-domain Icamentioning

confidence: 99%

An adaptive stereo basis method for convolutive blind audio source separation

et al. 2008

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“…A more common approach to the problem is frequency domain BSS (Smaragdis, 1997;Smaragdis, 1998;Anemueller and Kollmeier, 2000;Parra and Spence, 2000a;Zhou and Xu, 2003;Sawada et al, 2005). It is possible to use an appropriate Fourier Transform to equation…”

Section: Accepted Manuscriptmentioning

confidence: 99%

“…This problem can be omitted by applying an appropriate operation to the separation matrix that smoothes the separation matrices in the frequency domain. This can be obtained by reducing the filter length by a rectangular window as suggested by Smaragdis (1998), Parra (2000a), Schobben (2002) and Buchner (2004) or by averaging the separation matrices with neighbouring frequencies (Smaragdis, 1998). However, such an operation changes the separation matrix slightly and can also influence the final separation.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Speech intelligibility improvement using convolutive blind source separation assisted by denoising algorithms

Kociński

2008

Speech Communication

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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%

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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Blind separation of convolved mixtures in the frequency domain

Cited by 667 publications

References 9 publications

An adaptive stereo basis method for convolutive blind audio source separation

An adaptive stereo basis method for convolutive blind audio source separation

Speech intelligibility improvement using convolutive blind source separation assisted by denoising algorithms

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

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