Robust voice activity detection using gammatone filtering and entropy

Ong, Wei Qing; Tan, Alan Wee Chiat

doi:10.1109/icoras.2016.7872630

Cited by 5 publications

(8 citation statements)

References 10 publications

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“…Table 2. Average detection accuracy (%) at different signal-to-noise ratios of AMR-VAD2 [12], PND [9], SFF [25], LTSV [1], GE-VAD [24] and the proposed UEWE-DANF.…”

Section: Voice Activity Detection Results and Benchmarkingmentioning

confidence: 99%

“…The other three are the single-frequency filtering approach for discriminating speech and non-speech (SFF) [25], the formant-based robust voice activity detection (PND) [9] and the robust voice activity detection using long-term signal variability (LTSV) [1]. In addition, the gammatone filtering and entropy-based VAD (GE-VAD) proposed in [24] is also compared. The performance evaluation is carried out on noise-added speech signals under different signal-to-noise ratios (SNR).…”

Section: Methodsmentioning

confidence: 99%

“…As in our earlier paper [24], this paper also uses gammatone filter and extract weighted entropy at the front-end of the VAD to extract features that contain frequency-sensitive information of the signal. Unlike [24], which relies on the sampled signal to establish a constant noise floor and weight factors, this paper uses the asymmetric nonlinear filter (ANF) to generate adaptive weight factors.…”

Section: The Contributions Of This Articlementioning

confidence: 99%

“…However, its envelope computation is complex due to the large number of frequency channels required. The SFF-based VAD can be improved by replacing the SFF extraction approach with gammatone filter banks with the reduced channel numbers [24]. Besides using the gammatone filters, the mean and variance computation in SFF-based VAD are replaced with the entropy measure to improve the discrimination power of features.…”

Section: Vad Systems and Performance Measurementmentioning

confidence: 99%

“…Unlike [24], which relies on the sampled signal to establish a constant noise floor and weight factors, this paper uses the asymmetric nonlinear filter (ANF) to generate adaptive weight factors. ANF is used as an upper envelope detector in the calculation of adaptive weight factors, which is subsequently used to obtain the weighted entropy.…”

Section: The Contributions Of This Articlementioning

confidence: 99%

See 4 more Smart Citations

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

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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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“…Table 2. Average detection accuracy (%) at different signal-to-noise ratios of AMR-VAD2 [12], PND [9], SFF [25], LTSV [1], GE-VAD [24] and the proposed UEWE-DANF.…”

Section: Voice Activity Detection Results and Benchmarkingmentioning

confidence: 99%