Binaural semi-blind dereverberation of noisy convoluted speech signals

Lee, Jong‐Hwan; Oh, Sang-Hoon; Lee, Sang Yup

doi:10.1016/j.neucom.2008.07.005

Cited by 7 publications

(4 citation statements)

References 14 publications

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“…non-Gaussian) spectral features of the signal. 242 Another feature used is the spatial covariance of a microphone array The direct-arriving sound (i.e. non-reverberant) is strongly correlated across two spatially separated microphones, as the signal detected at each channel is the same signal with different time delays.…”

Section: A Reverberationmentioning

confidence: 99%

Machine learning in acoustics: Theory and applications

Bianco

Gerstoft

Traer

et al. 2019

The Journal of the Acoustical Society of America

424

126

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Acoustic data provide scientific and engineering insights in fields ranging from biology and communications to ocean and Earth science. We survey the recent advances and transformative potential of machine learning (ML), including deep learning, in the field of acoustics. ML is a broad family of statistical techniques for automatically detecting and utilizing patterns in data. Relative to conventional acoustics and signal processing, ML is data-driven. Given sufficient training data, ML can discover complex relationships between features and desired labels or actions, or between features themselves. With large volumes of training data, ML can discover models describing complex acoustic phenomena such as human speech and reverberation. ML in acoustics is rapidly developing with compelling results and significant future promise. We first introduce ML, then highlight ML developments in five acoustics research areas: source localization in speech processing, source localization in ocean acoustics, bioacoustics, seismic exploration, and environmental sounds in everyday scenes.

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Section: A Reverberationmentioning

confidence: 99%

Machine learning in acoustics: Theory and applications

Bianco

Gerstoft

Traer

et al. 2019

The Journal of the Acoustical Society of America

424

126

View full text Add to dashboard Cite

show abstract

“…In [12], spectral subtraction is proposed to suppress late-reverberation for the binaural signals [1], later adopted by [13] and [14]. Filtering is also employed for binaural dereverberation [15].…”

Section: Introductionmentioning

confidence: 99%

Preserving the beamforming effect for spatial cue-based pseudo-binaural dereverberation of a single source

Gul

Khan

Shah

2023

Computer Speech & Language

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“…Wu and Wang [5] proposed to maximize the kurtosis of the linear prediction (LP) residual of the original clean speech and then to use a spectral subtraction algorithm to decrease late reverberation. Lee et al [6] resorted to a binaural (two-channel) model to reformulate the problem of blind dereverberation as a single-input multiple-output (SIMO) inverse filtering problem. Jointly reducing spectral coloration due to late reverberant energy as well as background noise for speech enhancement in practical applications has also been the focus of other recent single- and multi-channel speech dereverberation strategies (e.g., see [7]–[9]).…”

Section: Introductionmentioning

confidence: 99%

Selective-Tap Blind Dereverberation for Two-Microphone Enhancement of Reverberant Speech

Kokkinakis

Loizou

2009

IEEE Signal Process. Lett.

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In this letter we propose a novel approach for two-microphone enhancement of speech corrupted by reverberation. Our approach steers computational resources to filter coefficients having the largest impact on the error surface and therefore only updates a subset of coefficients in every iteration. Experimental results carried out in a realistically reverberant setup indicate that the performance of the proposed algorithm is comparable to the performance of its full-update counterpart. Index TermsBlind dereverberation; SIMO-FIR model; least-mean-square (LMS); selective-tap filter updating; perceptual evaluation of speech quality (PESQ)

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Binaural semi-blind dereverberation of noisy convoluted speech signals

Cited by 7 publications

References 14 publications

Machine learning in acoustics: Theory and applications

Machine learning in acoustics: Theory and applications

Preserving the beamforming effect for spatial cue-based pseudo-binaural dereverberation of a single source

Selective-Tap Blind Dereverberation for Two-Microphone Enhancement of Reverberant Speech

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