A Convolutional Neural Network Smartphone App for Real-Time Voice Activity Detection [PDF]
IEEE Access, 2018 This paper presents a smartphone app that performs real-time voice activity detection based on convolutional neural network. Real-time implementation issues are discussed showing how the slow inference time associated with convolutional neural networks ...
Abhishek Sehgal, Nasser Kehtarnavaz
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NeuroVAD: Real-Time Voice Activity Detection from Non-Invasive Neuromagnetic Signals [PDF]
Sensors, 2020 Neural speech decoding-driven brain-computer interface (BCI) or speech-BCI is a novel paradigm for exploring communication restoration for locked-in (fully paralyzed but aware) patients.
Debadatta Dash +3 more
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Dual Microphone Voice Activity Detection Based on Reliable Spatial Cues [PDF]
Sensors, 2019 Two main spatial cues that can be exploited for dual microphone voice activity detection (VAD) are the interchannel time difference (ITD) and the interchannel level difference (ILD).
Soojoong Hwang +2 more
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Using Voice Activity Detection and Deep Neural Networks with Hybrid Speech Feature Extraction for Deceptive Speech Detection [PDF]
Sensors, 2022 In this work, we first propose a deep neural network (DNN) system for the automatic detection of speech in audio signals, otherwise known as voice activity detection (VAD).
Serban Mihalache, Dragos Burileanu
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A Hierarchical Framework Approach for Voice Activity Detection and Speech Enhancement [PDF]
The Scientific World Journal, 2014 Accurate and effective voice activity detection (VAD) is a fundamental step for robust speech or speaker recognition. In this study, we proposed a hierarchical framework approach for VAD and speech enhancement.
Yan Zhang +3 more
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Voice Activity Detection Using Fuzzy Entropy and Support Vector Machine
Entropy, 2016 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 ...
R. Johny Elton, P. Vasuki, J. Mohanalin
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A Novel Voice Activity Detection for Multi-Channel Noise Reduction
IEEE Access, 2021 In this study, a voice activity detection technique is designed using features such as short-term energy, periodicity and spectral flatness. The desired results are obtained by using these three features, even at low signal to noise ratio values.
Ramazan Colak, Rafet Akdeniz
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Sensors, 2023 Singing-voice separation is a separation task that involves a singing voice and musical accompaniment. In this paper, we propose a novel, unsupervised methodology for extracting a singing voice from the background in a musical mixture.
Feng Li, Yujun Hu, Lingling Wang
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Voice Activity Detection using Clustering-based Method in Spectro-Temporal Features Space [PDF]
Journal of Artificial Intelligence and Data Mining, 2022 This paper proposes a novel method for voice activity detection based on clustering in spectro-temporal domain. In the proposed algorithms, auditory model is used to extract the spectro-temporal features.
N. Esfandian +2 more
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AUC optimization for deep learning-based voice activity detection
EURASIP Journal on Audio, Speech, and Music Processing, 2022 Voice activity detection (VAD) based on deep neural networks (DNN) have demonstrated good performance in adverse acoustic environments. Current DNN-based VAD optimizes a surrogate function, e.g., minimum cross-entropy or minimum squared error, at a given
Xiao-Lei Zhang, Menglong Xu
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