Video-Aided Model-Based Source Separation in Real Reverberant Rooms

Khan, Muhammad Salman; Naqvi, Syed Mohsen; ur-Rehman, Ata; Wang, Wenwu; Chambers, Jonathon A.

doi:10.1109/tasl.2013.2261814

Cited by 29 publications

(10 citation statements)

References 36 publications

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“…The dereverberation scheme is based on spectral subtraction [12]. In the second stage we employ an efficient model-based source separation technique which is motivated by aspects of the human auditory system by Mapped vector x in a feature space α Dual variables kðÁ; ÁÞ Kernel function combining the models of interaural level difference (ILD), interaural phase difference (IPD) and the model of mixing vectors [11]. This probabilistic modeling, which is performed in the time-frequency (TF) domain, yields TF soft masks for each source in the mixture that can be used for their reconstruction.…”

Section: Interference Suppressionmentioning

confidence: 99%

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An unsupervised acoustic fall detection system using source separation for sound interference suppression

et al. 2015

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We present a novel unsupervised fall detection system that employs the collected acoustic signals (footstep sound signals) from an elderly person’s normal activities to construct a data description model to distinguish falls from non-falls. The measured acoustic signals are initially processed with a source separation (SS) technique to remove\ud the possible interferences from other background sound sources. Mel-frequency cepstral coeﬃcient (MFCC) features are next extracted from the processed signals and used to construct a data description model based on a one class support vector machine (OCSVM) method, which is ﬁnally applied to distinguish fall from non-fall sounds. Experiments on a recorded dataset conﬁrm that our proposed fall detection system can achieve better performance, especially with high level of interference from other sound sources, as compared with existing single microphone based methods

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Section: Interference Suppressionmentioning

confidence: 99%

“…The ILD, IPD and mixing vector models are combined and the model parameters are estimated in the maximum likelihood sense using iterative expectation-maximization (EM) [11]. TF masks are generated after a fixed number of iterations and then used to reconstruct the acoustic signals from different sources.…”

Section: Source Localization and Separationmentioning

confidence: 99%

An unsupervised acoustic fall detection system using source separation for sound interference suppression

et al. 2015

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“…We used our recently proposed source separation (SS) algorithm [12] for this purpose. BRIRs used in this experiment [10] were measured in four different rooms with RT60s of 0.32, 0.47, 0.68, and 0.89 seconds.…”

Section: Dereverberation and Source Separationmentioning

confidence: 99%

A new cascaded spectral subtraction approach for binaural speech dereverberation and its application in source separation

Khan

Naqvi

Chambers

2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

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“…The complementary information of AV data is truly adopted in audio-visual speech recognition (AVSR) in either of early-(feature), middle-(model), or late-(decoding) stage fusion schemes to enhance robustness against acoustic distortions. In recent years (since 2001 [7]), researchers have proposed methods based on exploiting the coherent component of AV processes for applicable tasks like speech enhancement [7][8][9], acoustic feature enhancement [10], visual voice activity detection (VVAD) [11], and AV source separation (AVSS) [11][12][13][14][15][16][17][18][19][20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

“…Khan et al [24] have proposed a video-aided separation method for two-channel reverberant recordings which estimates direction of sources via visual localization to be used in probabilistic models which are refined using EM algorithm and evaluated at discrete TF points to generate separating masks.…”

Section: Introductionmentioning

confidence: 99%

Audio visual speech source separation via improved context dependent association model

Kazemi

Boostani

Sobhanmanesh

2014

EURASIP J. Adv. Signal Process.

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In this paper, we exploit the non-linear relation between a speech source and its associated lip video as a source of extra information to propose an improved audio-visual speech source separation (AVSS) algorithm. The audio-visual association is modeled using a neural associator which estimates the visual lip parameters from a temporal context of acoustic observation frames. We define an objective function based on mean square error (MSE) measure between estimated and target visual parameters. This function is minimized for estimation of the de-mixing vector/filters to separate the relevant source from linear instantaneous or time-domain convolutive mixtures. We have also proposed a hybrid criterion which uses AV coherency together with kurtosis as a non-Gaussianity measure. Experimental results are presented and compared in terms of visually relevant speech detection accuracy and output signal-to-interference ratio (SIR) of source separation. The suggested audio-visual model significantly improves relevant speech classification accuracy compared to existing GMM-based model and the proposed AVSS algorithm improves the speech separation quality compared to reference ICA-and AVSS-based methods.

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Video-Aided Model-Based Source Separation in Real Reverberant Rooms

Cited by 29 publications

References 36 publications

An unsupervised acoustic fall detection system using source separation for sound interference suppression

An unsupervised acoustic fall detection system using source separation for sound interference suppression

A new cascaded spectral subtraction approach for binaural speech dereverberation and its application in source separation

Audio visual speech source separation via improved context dependent association model

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