Robust Voice Activity Detection Using Long-Term Signal Variability

Ghosh, Prasanta Kumar; Tsiartas, Andreas; Narayanan, Shrikanth

doi:10.1109/tasl.2010.2052803

Cited by 164 publications

(125 citation statements)

References 21 publications

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“…Non-speech segments include silences, unwanted utterances or background noise from crowds, machinery, aircraft, in the interior of moving vehicles, etc. [1]. Voice activity detection is an important component in speech signal processing for speech recognition and noise reduction.…”

Section: Voice Activity Detectionmentioning

confidence: 99%

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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: Voice Activity Detectionmentioning

confidence: 99%

“…The gammatone filter bank simulates the frequency analysis capability of the basilar membrane by having several gammatone filters with different center frequencies. The gammatone filter is a linear filter described by an impulse response formed by multiplying the gamma distribution with a sinusoidal tone as shown in Equation (1).…”

Section: Gammatone Filtermentioning

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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show abstract

“…The histogram based SSAP estimation is inspired by long-term signal variability with adaptive thresholding [33]. We take the longterm histogram of P [i] (f ) for each i after elimination of invalid frames when P [i] (f ) = 0.…”

Section: ) Estimation Of Ssap By Spectrum Histogrammentioning

confidence: 99%

Blind spatial sound source clustering and activity detection using uncalibrated microphone array

Nakamura

Mizumoto

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-This paper presents a method for estimating the number, as well as the activity periods of spatially distributed sound sources using an uncalibrated microphone array. This methodology is applied for the purposes of speaker diarization. In general, speaker diarization has difficulty with: 1) estimating the number of sound sources (speakers), and 2) activity detection of multiple sound sources including overlap of utterances. Several microphone array based techniques have already tackled these challenges. However, existing methods mainly assume that the steering vectors for the microphone array are calibrated in advance to identify sound sources, which is difficult to satisfy when ad-hoc or flexible microphone arrays are used. Thus our approach estimates the number of sound sources blindly in two steps. First, Time Delay of Arrival (TDOA) of the observed signal is clustered. Second, the sound source activity is detected by clustering the long-term spatial spectrum using the TDOA based steering vector for each cluster. The validity of the algorithm is confirmed by both synthesized signals and a real-world flexible microphone array application.

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“…In subjective evaluation, a human listener evaluates for VAD errors, whereas, numerical computations are carried out for objective evaluation. However, subjective evaluation alone is insufficient to examine the VAD performance, because listening tests like ABC fail to consider the effects of false alarm [32,34,35]. Hence numerical computations through objective evaluation help in reporting the performance of the proposed VAD algorithm.…”

Section: Performance Evaluationmentioning

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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Robust Voice Activity Detection Using Long-Term Signal Variability

Cited by 164 publications

References 21 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

Blind spatial sound source clustering and activity detection using uncalibrated microphone array

Voice Activity Detection Using Fuzzy Entropy and Support Vector Machine

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