Single Frequency Filtering Approach for Discriminating Speech and Nonspeech

Aneeja, G.; Yegnanarayana, B.

doi:10.1109/taslp.2015.2404035

Cited by 90 publications

(71 citation statements)

References 19 publications

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“…The first is the voice activity detection for adaptive multi-rate Option 2 (denoted as AMR-VAD2) that was standardized for the European Telecommunications Standards Institute (ETSI) [12]. 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.…”

Section: Methodsmentioning

confidence: 99%

“…Similar to the SFF approach, GE-VAD uses a weight factor to compensate the effect of the noise floor. The authors in [25] assumed that the noise floor of the SFF amplitude envelope can be modeled using the lowest twenty percent of the samples. This approach is inappropriate for continuous real-time VADs.…”

Section: Vad Systems and Performance Measurementmentioning

confidence: 99%

“…In [25], signal amplitude envelopes are computed at equally-distributed center frequencies with a constant bandwidth of 20 Hz. This distribution of center frequencies does not realistically represent the resonances present in the basilar membrane.…”

Section: Gammatone Filtermentioning

confidence: 99%

“…Unlike most of the conventional noise suppression methods, which filter noise with a fixed value estimated from the noise floor [25], an adaptive noise filtering approach is more appropriate in filtering a time-varying noise floor. The asymmetric nonlinear filter is one of the approaches used to estimate the changing noise floor, which is necessary for noise suppression.…”

Section: Asymmetric Nonlinear Filtermentioning

confidence: 99%

“…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%

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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: Methodsmentioning

confidence: 99%

Section: Vad Systems and Performance Measurementmentioning

confidence: 99%

Section: Gammatone Filtermentioning

confidence: 99%

Section: Asymmetric Nonlinear Filtermentioning

confidence: 99%

Section: Performance Evaluation Metricsmentioning

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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A novel double pole transfer function‐single frequency filtering approach for speech enhancement

Srinivasarao

Ghanekar

2020

Trans Emerging Tel Tech

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Speech intelligibility improvement is a major field of recent research with in audiology and hearing domain. Speech signal usually gets affected due to various environmental and background noise. Several approaches for speech intelligibility improvement were presented in the literature. In this paper, a method of speech enhancement based on Double Pole Transfer Function-Single Frequency Filter (DPTF-SFF) has been proposed. In the SFF approach the temporal and spectral resolutions are controlled with a one particular value of the filter that corresponds to the pole position in the complex z-plane. The proposed method uses double pole transfer function SFF that improves the magnitude and phase for various frequencies which are used in the synthesis of the original signal. In the analysis of this method clean speech signals are used from GRID corpus database and noise signals are taken from NOIZEUS and NOISEX-92 databases. The performance evaluation of this method is carried out in terms of Perceptual Evaluation of Speech Quality, Short-Time Objective Intelligibility, and Segmental Signal to Noise Ratio metrics. The implementation results proved that the proposed method is an efficient one as it gives higher intelligibility scores compared to the existing methods at different SNR levels (dB) and for different noises employed.

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An Unsupervised Voice Activity Detection Using Time-Frequency Features

Mait¹,

Aboutabit²

2023

Lecture Notes in Networks and Systems

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Single Frequency Filtering Approach for Discriminating Speech and Nonspeech

Cited by 90 publications

References 19 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

A novel double pole transfer function‐single frequency filtering approach for speech enhancement

An Unsupervised Voice Activity Detection Using Time-Frequency Features

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