The voice activity detector for the Pan-European digital cellular mobile telephone service

Freeman, Darren; Cosier, G.; Southcott, Clare; Boyd, I.

doi:10.1109/icassp.1989.266442

Cited by 106 publications

(48 citation statements)

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“…Freeman et al proposed a set of metrics in [36], which are widely used to evaluate the performance of the voice activity detector [1,25,37]. Specifically, the five metrics are illustrated in Figure 11 and defined thereafter.…”

Section: Performance Evaluation Metricsmentioning

confidence: 99%

Real-Time Robust Voice Activity Detection Using the Upper Envelope Weighted Entropy Measure and the Dual-Rate Adaptive Nonlinear Filter

Ong

Tan

Vengadasalam

et al. 2017

Entropy

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Voice activity detection (VAD) is a vital process in voice communication systems to avoid unnecessary coding and transmission of noise. Most of the existing VAD algorithms continue to suffer high false alarm rates and low sensitivity when the signal-to-noise ratio (SNR) is low, at 0 dB and below. Others are developed to operate in offline mode or are impractical for implementation in actual devices due to high computational complexity. This paper proposes the upper envelope weighted entropy (UEWE) measure as a means to enable high separation of speech and non-speech segments in voice communication. The asymmetric nonlinear filter (ANF) is employed in UEWE to extract the adaptive weight factor that is subsequently used to compensate the noise effect. In addition, this paper also introduces a dual-rate adaptive nonlinear filter (DANF) with high adaptivity to rapid time-varying noise for computation of the decision threshold. Performance comparison with standard and recent VADs shows that the proposed algorithm is superior especially in real-time practical applications.

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Section: Performance Evaluation Metricsmentioning

confidence: 99%

Real-Time Robust Voice Activity Detection Using the Upper Envelope Weighted Entropy Measure and the Dual-Rate Adaptive Nonlinear Filter

Ong

Tan

Vengadasalam

et al. 2017

Entropy

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“…For evaluating the performance of the proposed VAD algorithm we follow the objective evaluation methods [4], where labels obtained by the VAD is compared against true reference labels. Objective evaluation can be done in two ways -accuracy rate and error rate.…”

Section: B Evaluation Metricmentioning

confidence: 99%

“…It is mainly used to achieve high recognition rate or system accuracy by removing insignificant parts while processing the signal. It is also used in real time communication systems [4] and speech encoder [5] to attain high compression rate and low transmission rate.…”

Section: Introductionmentioning

confidence: 99%

Robust Voice Activity Detection Algorithm based on Long Term Dominant Frequency and Spectral Flatness Measure

Singh¹,

Chanu²

2017

IJIGSP

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Abstract-In this paper, a robust voice activity detection algorithm based on a long-term metric using dominant frequency and spectral flatness measure is proposed. The propose algorithm makes use of the discriminating power of both features to derive the decision rule. This method reduces the average number of speech detection errors. We evaluate its performance using 15 additive noises at different SNRs (-10 dB to 10 dB) and compared with some of the most recent standard algorithms. Experiments show that our propose algorithm achieves the best performance in terms of accuracy rate average over all SNRs and noises.Index Terms-Voice activity detection, dominant frequency component, spectral flatness measure.

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“…VAD stands as a preprocessing stage for major speech processing applications. The applications of VAD extend to mobile communications [3], transmitting speech signals over internet [4], and suppressing noises in digital hearing aids [5].…”

Section: Introductionmentioning

confidence: 99%

Voice Activity Detection Using Fuzzy Entropy and Support Vector Machine

Elton¹,

Vasuki²,

Mohanalin³

2016

Entropy

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This paper proposes support vector machine (SVM) based voice activity detection using FuzzyEn to improve detection performance under noisy conditions. The proposed voice activity detection (VAD) uses fuzzy entropy (FuzzyEn) as a feature extracted from noise-reduced speech signals to train an SVM model for speech/non-speech classification. The proposed VAD method was tested by conducting various experiments by adding real background noises of different signal-to-noise ratios (SNR) ranging from −10 dB to 10 dB to actual speech signals collected from the TIMIT database. The analysis proves that FuzzyEn feature shows better results in discriminating noise and corrupted noisy speech. The efficacy of the SVM classifier was validated using 10-fold cross validation. Furthermore, the results obtained by the proposed method was compared with those of previous standardized VAD algorithms as well as recently developed methods. Performance comparison suggests that the proposed method is proven to be more efficient in detecting speech under various noisy environments with an accuracy of 93.29%, and the FuzzyEn feature detects speech efficiently even at low SNR levels.

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The voice activity detector for the Pan-European digital cellular mobile telephone service

Cited by 106 publications

References 0 publications

Real-Time Robust Voice Activity Detection Using the Upper Envelope Weighted Entropy Measure and the Dual-Rate Adaptive Nonlinear Filter

Real-Time Robust Voice Activity Detection Using the Upper Envelope Weighted Entropy Measure and the Dual-Rate Adaptive Nonlinear Filter

Robust Voice Activity Detection Algorithm based on Long Term Dominant Frequency and Spectral Flatness Measure

Voice Activity Detection Using Fuzzy Entropy and Support Vector Machine

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