Wavelet energy based voice activity detection and adaptive thresholding for efficient speech coding

Joseph, Shijo M.; Babu, Anto P.

doi:10.1007/s10772-014-9240-x

Cited by 8 publications

(3 citation statements)

References 8 publications

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“…In this paper, a VAD based on DWT is considered since this transform is already computed as part of the noise classification pipeline, thus limiting the computational burden on the overall system. DWT-based VADs have been explored in the literature such as [7] but in this work, it is demonstrated that a fairly robust VAD can be achieved from simple energy comparisons at the frame and sub-band levels.…”

Section: B Voice Activity Detectionmentioning

confidence: 99%

A Discrete Wavelet Transform-Based Voice Activity Detection and Noise Classification with Sub-Band Selection

Abdullah

Zamani

Demosthenous

2021

2021 IEEE International Symposium on Circuits and Systems (ISCAS)

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A real-time discrete wavelet transform-based adaptive voice activity detector and sub-band selection for feature extraction are proposed for noise classification, which can be used in a speech processing pipeline. The voice activity detection and sub-band selection rely on wavelet energy features and the feature extraction process involves the extraction of melfrequency cepstral coefficients from selected wavelet sub-bands and mean absolute values of all sub-bands. The method combined with a feedforward neural network with two hidden layers could be added to speech enhancement systems and deployed in hearing devices such as cochlear implants. In comparison to the conventional short-time Fourier transformbased technique, it has higher F1 scores and classification accuracies (with a mean of 0.916 and 90.1%, respectively) across five different noise types (babble, factory, pink, Volvo (car) and white noise), a significantly smaller feature set with 21 features, reduced memory requirement, faster training convergence and about half the computational cost.

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Section: B Voice Activity Detectionmentioning

confidence: 99%

A Discrete Wavelet Transform-Based Voice Activity Detection and Noise Classification with Sub-Band Selection

Abdullah

Zamani

Demosthenous

2021

2021 IEEE International Symposium on Circuits and Systems (ISCAS)

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“…For threshold processing function, the hard threshold is easy to appear the pseudo‐Gibbs effects and while the wavelet coefficient is larger than the threshold, soft threshold wavelet comes out a constant deviation [38, 39]. To improve the inherent defects of soft and hard threshold methods, Gao and Bruce [40, 41] jointly proposed a semi‐soft threshold function and the Minimax threshold processing method.…”

Section: Related Workmentioning

confidence: 99%

Restoration algorithm for noisy complex illumination

Liu

Gao

Kong

et al. 2018

IET Computer Vision

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Although promising results have been achieved in the restoration of complex illumination images with the Retinex algorithm, there are still some drawbacks in the processing of Retinex. Considering the noise characteristics of complex illumination images, in this study, we propose a novel restoration algorithm for noisy complex illumination, which combines guided adaptive multi-scale Retinex (GAMSR) and improvement BayesShrink threshold filtering (IBTF) based on double-density dual-tree complex wavelet transform (DDDTCWT) domain. Extensive restoration experiments are conducted on three typical types images and the same image with different noises. On the basis of a series of evaluation indexes, we compare our method to those of state-of-the-art algorithms. The results show that (i) SSIM of the proposed IBTF is superior to traditional Bayes threshold method by 15% as the standard variance is 100. (ii) PSNR of the proposed GAMSR enhances 15% to traditional MSR. (iii) The clarity of final results for restoration speeds up three times than that of original images, and the information entropy is improved slightly too. Therefore, the proposed method can effectively enhance the details, edges and textures of the image under complex illumination and noises.

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“…Voice activity detection (VAD), i.e., detecting the presence or absence of human speech, is crucial for several speech processing applications, such as noisy speech enhancement [1], speaker recognition [2], speech coding systems [3], echo cancellation and hands-free telephony [4]. VAD algorithms trade off noise sensitivity, precision, and computational complexity and consist of two consecutive phases, namely, speechrelated feature extraction and a discriminating model.…”

Section: Introductionmentioning

confidence: 99%

Robust distributed multi-speaker voice activity detection using stability selection for sparse non-negative feature extraction

Hamaidi

Muma

Zoubir

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-In this paper, we propose a robust multi-speaker voice activity detection approach for wireless acoustic sensor networks (WASN). Each node of the WASN receives a mixture of sound sources. We propose a non-negative feature extraction using stability selection that exploits the sparsity of the speech energy signals. The strongest right singular vectors serve as source-specific features for the subsequent voice activity detection (VAD). To separate active speech frames from silent frames, we propose a robust Mahalanobis classifier that is based on an M-estimator of the covariance matrix. The proposed approach can also be applied to a distributed setting, where no fusion center is available. Highly accurate VAD results are obtained in a challenging WASN of 20 nodes observing 6 sources in a reverberant environment.

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Wavelet energy based voice activity detection and adaptive thresholding for efficient speech coding

Cited by 8 publications

References 8 publications

A Discrete Wavelet Transform-Based Voice Activity Detection and Noise Classification with Sub-Band Selection

A Discrete Wavelet Transform-Based Voice Activity Detection and Noise Classification with Sub-Band Selection

Restoration algorithm for noisy complex illumination

Robust distributed multi-speaker voice activity detection using stability selection for sparse non-negative feature extraction

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