Voice activity detection (VAD) is a technique to detect whether a sound signal belongs to speech or non-speech based on the statistical distribution of acoustic features. It plays an important role in front-end processing for various speech applications such as speech enhancement, robust speech recognition systems, and speaker recognition. Here, we have compiled several VAD-related research papers and some their corresponding codes, starting from 198*. Scholars and engineers in need can refer to them for learning purposes. Welcome anyone who is interested to add research papers.
Recently, I have been using a script to synthesize audio using an open-source noise dataset and clean speech dataset for the purpose of training and decoding in deep learning. This script will also be made open-source. Of course, if you have a better script, please feel free to contribute it to this project.
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[Classification] (## Classification)
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[Code ] (## Code)
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[Dataset ] (## Dataset)
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[References ] (## References)
| Method | Feature | Concept | Work Environment |
|---|---|---|---|
| G.729B VAD [6, 24] | linear spectrum frequency, zero crossing rate, full band signal energy, low band signal energy | Harmonicity | Noisy, High SNR |
| Short term feature -VAD [1,3,51] | ZCR, energy, correlation function, Pitch detection | Short term speech features | Quiet |
| Wavelet - based VAD [7,37,83] | Wavelet，wavelet entropy, perceptual wavelet packet decomposition | Wavelet | Noisy, High SNR |
| Entropy based VAD [20,22,30,45,82,89] | Spectral entropy, energy, spectrum | Entropy | Noisy, Stable noise |
| AMR VAD.1 [10,11,24] | pitch period, SNR, tone detection, Complex signal analysis and detection | Sub -band analysis | Noisy, high SNR |
| AMR VAD.2 [10,11,24] | channel energy, channel SNR, voice metric, frame SNR, long-term SNR | Sub-band analysis | Noisy, high SNR |
| Cepstrum based [2,4,18] | MFCC, PLCC | Cepstrum | Noisy / stationary noise |
| Spectral Peaks-based [52,57] | Spectral Peaks feature | Spectral Peaks | Noisy |
| Speech enhancement (spectral subtraction) based VAD [56] | Energy | Speech enhancement two steps processing | Noisy |
| MTF - VAD [71,86] | Temporal power envelope | MTF | Reverberant / stationary noise |
| EMD - based VAD [66,80] | empirical mode decomposition and modulation spectrum analysis | EMD | Noisy/Stationary noise |
| LSTV/LSFM -VAD [58,69, 79, 85] | degree of non-stationarity, Auto-correlation, spectral flatness, spectral variation | Long term variation | Noisy, unstationary noise |
| Kalman filter-based [48] | log-Mel spectral | Kalman filter | Noisy |
| HMM/Bayesian/GMM/clustering/spectral clustering(unsupervised) -based VAD [12, 13, 21, 36,37,38,47,61,68, 75,81] | MFCC, correlation function, energy, spectra-gram, wavelet, Mel-subband | Statistics (Unsupervised, supervised) | Noisy, stationary, unstationary |
| LDA -based VAD [33] | Frequency Filtering features | LDA | Reverberant |
| SVM - based VAD [27,44,67,89] | MFCC, Entropy, spectral distortion, full-band energy difference, low-band energy difference, the zero-crossing difference | SVM | Noisy |
| DNN/CNN/LSTM based VAD [72,82,92,94,95,97, 102, 76,77,84,88,91,96,200,201] | Pitch, MFCC, LPC, PLP phase, and spectra-gram. | Deep learning | Noisy / unstable noise |
My modified MATLAB code: https://github.com/linan2/VAD_MATLAB.git
A effective VAD code when I am writing paper (rVAD)：https://github.com/zhenghuatan/rVAD.git
Sohn VAD (some paper use this method to label, however I tried rVAD is better): https://github.com/eesungkim/Voice_Activity_Detector
Alibaba modelscope (my friend told me it is effective): https://www.modelscope.cn/models/damo/speech_fsmn_vad_zh-cn-16k-common-pytorch/summary
fsmn vad https://github.com/lovemefan/fsmn-vad.git
A VAD project: https://github.com/snakers4/silero-vad.git
Speech brain: [(https://github.com/speechbrain/speechbrain/tree/develop/recipes/LibriParty/VAD)]
I like its MRCG feature to do experiments (this code is too old). : https://github.com/jtkim-kaist/VAD.git
I haven't tried this method, so I cannot assess its performance. If you have a need, you can try it: https://github.com/Cruze-Young/LTPD-VAD
Musan: http://www.openslr.org/17/
Noisex92:
Non-Speech-100
Rir_noise: http://www.openslr.org/28/ code: https://github.com/linan2/add_reverb2
Demond
DNS challenge: https://github.com/microsoft/DNS-Challenge
WSJ
timit:
ted: http://www.openslr.org/7/
Librispeech: http://www.openslr.org/12/
AISHELL: http://www.openslr.org/33/
[1]Freeman, D.K.; Southcott, C.B.; Boyd, I.; Cosier, G. A voice activity detector for pan-European digital cellular mobile telephone service. In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, Glasgow, Scotland, 23–26 May 1989; pp. 369–372
[2]J-C Junqua, Hisashi Wakita, "A comparative study of cepstral lifters and distance measures for all pole models of speech in noise", Proc. ICASSP, pp. 476-479, 1989. （cepstral coefficient）
[3]R Tucker, "Voice activity detection using a periodicity measure", IEE Proceedings I (Communications Speech and Vision), vol. 139, no. 4, pp. 377-380, 1992. （pitch detection）
[4]Haigh, J.A.; Mason, J.S. Robust voice activity detection using cepstral features. In Proceedings of the IEEE Region 10 Conference on Computer, Communication, Control and Power Engineering, Beijing, China,19–21 October 1993; pp. 321–324.
[5]Haigh, J.A. & Mason, John. (1993). Robust voice activity detection using cepstral features. IEEE TEN-CON. 321 - 324 vol.3. 10.1109/TENCON.1993.327987.
[6]ITU, Coding of Speech and 8 kbit/s Using Conjugate Structure Algebraic Code -Excited Linear Prediction. Annex B: A Silence Compression Scheme for G.729 Optimized for Terminals Conforming to Recommend. V.70, International Telecommunication Union, 1996.
[7]Stegmann J, Schroder G. Robust voice-activity detection based on the wavelet transform[C]// IEEE Workshop on Speech Coding for Telecommunications Proceeding. IEEE, 1997.
[8]Itoh, K.; Mizushima, M. Environmental noise reduction based on speech/non-speech identification for hearing aids. In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, Munich, Germany, 21–24 April 1997; pp. 419–422.
[9]R. Sarikaya and J. H. L. Hansen, “Robust speech activity detection in the presence of noise,” in Proc. 5th Int. Conf. Spoken Language Processing,1997, pp. 922–925.
[10]Adaptive Multi Rate (AMR) Speech; ANSI-C code for AMR Speech Codec, 1998.
[11]Digital Cellular Telecommunications System (Phase 2+); Adaptive Multi Rate (AMR); Speech Processing Functions; General Description,1998
[12]J. Sohn and W. Sung, “A voice activity detector employing soft decision based noise spectrum adaptation,” Proc. IEEE Int. Conf. Acoust., Speech, Signal Process., pp. 365–368, 1998
[13]J. Sohn and W. Sung, “A voice activity detector employing soft deci-sion based noise spectrum adaptation,” in Proc. IEEE ICASSP’98, vol.1, Seattle, WA, 1998, pp. 365–368.
[14]Sohn J , Kim N S , Sung W . A statistical model-based voice activity detection[J]. IEEE Signal Processing Letters, 1999, 6(1):1-3.
[15]Malah D . System and method for noise threshold adaptation for voice activity detection in nonstationary noise environments[J]. Journal of the Acoustical Society of America, 2000, 108(3):885.
[16]Press E . Method and device for voice activity detection and a communication device[J]. Journal of the Acoustical Society of America, 2000, 108(1):21.
[17]Mekuria F . Non-parametric voice activity detection: US 2000.
[18]Nemer, E.; Goubron, R.; Mahmoud, S. Robust voice activity detection using higher-order statistics in the LPC residual domain. IEEE Trans. Speech Audio Process. 2001,9, 217–231.
[19]E. Nemer, R. Goubran, S. Mahmoud, "Robust voice activity detection using higher-order statistics in the LPC residual domain", IEEE Trans. Speech Audio Process., vol. 9, no. 3, pp. 217-231, 2001.
[20]F. Beritelli, S. Casale, and G. Ruggeri, “Performance evaluation and comparison of ITU-T/ETSI voice activity detectors,” in Proc. IEEE ICASSP’01, vol. 3, Salt Lake City, UT, 2001, pp. 1425–1428.
[21]Y. D. Cho, K. Al-Naimi, and A. Kondoz, “Improved statistical voice activity detection based on a smoothed statistical likelihood ratio,” in Proc. IEEE ICASSP’01, vol. 2, Salt Lake City, UT, 2001, pp. 737–740
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[48]M. Fujimoto and K. Ishizuka, “Noise robust voice activity detection based on switching Kalman filter,” IEICE Trans. Inf. Syst., vol. 91, no. 3, pp. 467–477, 2008.
[49]R. J. Weiss and T. T. Kristjansson, “DySANA: Dynamic speech and noise adaptation for voice activity detection,” in Proc. Interspeech, 2008, pp. 127–130.
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[55]G.K. Choi and S.H. Kim, Voice activity detection method using psycho-acoustic model based on speech energy maxi-mization in noisy environments, Journal of the Acoustical Society of Korea 28 (2009), 447–453.
[56]Hsieh, C.-H.; Feng, T.-Y.; Huang, P.-C. Energy-based VAD with grey magnitude spectral subtraction. Speech Commun. 2009,51, 810–819.
[57]I.-C. Yoo and D. Yook, “Robust voice activity detection using the spectral peaks of vowel sounds,” ETRI J., vol. 31, pp. 451–453, Aug. 2009.
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[67]Ji Wu, Xiao-Lei Zhang, "Efficient multiple kernel support vector machine-based voice activity detection", IEEE Signal Process. Lett., vol. 18, no. 8, pp. 466-499, 2011.
[68]D. Ying, Y. Yan, J. Dang, and F. Soong, “Voice activity detection based on an unsupervised learning framework,” IEEE Trans. Audio, Speech, Lang. Process., vol. 19, no. 8, pp. 2624–2644, Nov. 2011.
[69]Ghosh, P.K.; Tsiartas, A.; Narayanan, S. Robust Voice Activity Detection Using Long-Term Signal Variability. IEEE Trans. Audio Speech Lang. Process. 2011,19, 600–613.
[70]Moattar M H , Homayounpour M M . A Weighted Feature Voting Approach for Robust and Real-Time Voice Activity Detection[J]. Etri Journal, 2011, 33(1):99–109.
[71]Unoki,M.,Lu,X.,Petrick,R.,Morita,S.,Akagi,M.,&Hoffmann, R. (2011). Voice activity detection in MTF-based power envelope restoration. In Proceedings Interspeech2011 (pp. 2609–2612).
[72]Zhang X L, Wu J. Deep Belief Networks Based Voice Activity Detection[J]. IEEE Transactions on Audio Speech and Language Processing, 2013, 21(4):697-710.
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[74]Shi-Wen Deng, Ji-Qing Han, "Statistical voice activity detection based on sparse representation over learned dictionary", Digital Signal Process., vol. 23, no. 4, pp. 1228-1232, 2013.
[75]Mousazadeh S , Cohen I . Voice Activity Detection in Presence of Transient Noise Using Spectral Clustering[J]. IEEE Transactions on Audio Speech and Language Processing, 2013, 21(6):1261-1271.
[76]T. Hughes and K. Mierle, “Recurrent neural networks for voice activity detection,” in Proc. Int. Conf. Acoust., Speech, Signal Process., 2013, pp. 7378–7382.
[77]F. Eyben, F. Weninger, S. Squartini, and B. Schuller, “Real-life voice activity detectionwith LSTM recurrent neural networks and an application to Hollywoodmovies,” in Proc. Int. Conf. Acoust., Speech, Signal Process., 2013, pp. 483–487.
[78]Ma, Y.; Nishihara, A. Efficient voice activity detection algorithm using long-term spectral flatness measure. EURASIP J. Audio Speech Music Process. 2013,2013.
[79]Yanna Ma, Akinori Nishihara. Efficient voice activity detection algorithm using long-term spectral flatness measure[J]. 2013, 2013(1):87.
[80]Kanai Y , Unoki M . Robust voice activity detection using empirical mode decomposition and modulation spectrum analysis[C]// International Symposium on Chinese Spoken Language Processing. IEEE, 2013.
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[82]N. Ryant, M. Liberman, and J. Yuan, “Speech activity detection on YouTube using deep neural networks,” in Proc. Interspeech, 2013, pp. 728–731.
[83]Gihyoun L , Sung Dae N , Jin-Ho C , et al. Voice activity detection algorithm using perceptual wavelet entropy neighbor slope[J]. Bio-medical materials and engineering, 2014, 24(6):3295-301.
[84]S. Thomas, S. Ganapathy, G. Saon, and H. Soltau, “Analyzing convolutional neural networks for speech activity detection in mismatched acoustic conditions,” in Proc. Int. Conf. Acoust., Speech, Signal Process., 2014, pp. 2519–2523.
[85]Shi, W.; Zou, Y.; Liu, Y. Long-term auto-correlation statistics based on voice activity detection for strong noisy speech. In Proceedings of the 2014 IEEE China Summit & International Conference on Signal and Information Processing, Xi’an, China, 9–13 July 2014; pp. 100–104.
[86]Morita, Shota & Unoki, Masashi & lu, Xugang & Akagi, Masato. (2015). Robust Voice Activity Detection Based on Concept of Modulation Transfer Function in Noisy Reverberant Environments. Journal of Signal Processing Systems. 82. 10.1007/s11265-015-1014-4.
[87]Zhang Y , Wang K , Yan B . Speech endpoint detection algorithm with low signal-to-noise based on improved conventional spectral entropy[C]// Intelligent Control & Automation. IEEE, 2016.
[88]S. Meier and W. Kellermann, “Artificial neural network-based feature combination for spatial voice activity detection,” in Proc. Interspeech, 2016, pp. 2987–2991.
[89]Johny E R , Vasuki P , Mohanalin J . Voice Activity Detection Using Fuzzy Entropy and Support Vector Machine[J]. Entropy, 2016, 18(8):298-.
[90]R. Zazo, T. N. Sainath, G. Simko, and C. Parada, “Feature learning with raw-waveform CLDNNs for voice activity detection,” in Proc. Interspeech, 2016, pp. 8–12.
[91]J. Kim, J. Kim, S. Lee, J. Park, and M. Hahn, “Vowel based voice activity detection with LSTM recurrent neural network,” in Proc. 8th Int. Conf. Signal Process. Syst., 2016, pp. 134–137.
[92]F. Vesperini, P. Vecchiotti, E. Principi, S. Squartini, and F. Piazza, “Deep neural networks for multi-room voice activity detection: Advancements and comparative evaluation,” in Proc. Int. Joint Conf. Neural Netw., 2016, pp. 3391–3398.
[93]T. Drugman,Y. Stylianou,Y. Kida, and M. Akamine, “Voice activity detection: Merging source and filter-based information,” IEEE Signal Process. Lett., vol. 23, no. 2, pp. 252–256, Feb. 2016.
[94]X.-L. Zhang and D.-L. Wang, “Boosting contextual information for deep neural network based voice activity detection,” IEEE/ACM Trans. Audio, Speech, Lang. Process., vol. 24, no. 2, pp. 252–264, Feb. 2016.
[95]Inyoung Hwang, Hyung-Min Park, Joon-Hyuk Chang, "Ensemble of deep neural networks using acoustic environment classification for statistical model-based voice activity detection", Computer Speech & Lang., vol. 38, pp. 1-12, 2016.
[96]D. A. Silva, J. A. Stuchi, R. P. V. Violato, and L. G. D. Cuozzo, “Exploring convolutional neural networks for voice activity detection,” in Cognitive Technologies. Cham, Switzerland: Springer, 2017, pp. 37–47.
[97]Longbiao Wang, Khomdet Phapatanaburi, Zeyan Go, Seiichi Nakagawa, Masahiro Iwahashi, Jianwu Dang, "Phase aware deep neural network for noise robust voice activity detection", Proc. ICME, pp. 1087-1092, 2017.
[98]Kim J , Hahn M . Voice Activity Detection Using an Adaptive Context Attention Model[J]. IEEE Signal Processing Letters, 2018:1-1.
[99]Jong Hwan Ko, Josh Fromm, Matthai Philipose, Ivan Tashev, Shuayb Zarar, "Limiting numerical precision of neural networks to achieve real-time voice activity detection", Proc. ICASSP, pp. 2236-2240, 2018.
[100]Wissam A. Jassim, Naomi Harte, "Voice activity detection using neurograms", Proc. ICASSP, pp. 5524-5528, 2018.
[101]Youngmoon Jung, Younggwan Kim, Yeunju Choi, Hoirin Kim, "Joint learning using denoising variational autoencoders for voice activity detection", Proc. Interspeech, pp. 1210-1214, 2018.
[102]Z. Fan, Z. Bai, X. Zhang, S. Rahardja and J. Chen, "AUC Optimization for Deep Learning Based Voice Activity Detection," ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, United Kingdom, 2019, pp. 6760-6764.
[103]Freeman, D.K.; Southcott, C.B.; Boyd, I.; Cosier, G. A voice activity detector for pan-European digital cellular mobile telephone service. In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, Glasgow, Scotland, 23–26 May 1989; pp. 369–372
[104]J-C Junqua, Hisashi Wakita, "A comparative study of cepstral lifters and distance measures for all pole models of speech in noise", Proc. ICASSP, pp. 476-479, 1989. （cepstral coefficient）
[105]R Tucker, "Voice activity detection using a periodicity measure", IEE Proceedings I (Communications Speech and Vision), vol. 139, no. 4, pp. 377-380, 1992. （pitch detection）
[106]Haigh, J.A.; Mason, J.S. Robust voice activity detection using cepstral features. In Proceedings of the IEEE Region 10 Conference on Computer, Communication, Control and Power Engineering, Beijing, China,19–21 October 1993; pp. 321–324.
[107]Haigh, J.A. & Mason, John. (1993). Robust voice activity detection using cepstral features. IEEE TEN-CON. 321 - 324 vol.3. 10.1109/TENCON.1993.327987.
[108]ITU, Coding of Speech and 8 kbit/s Using Conjugate Structure Algebraic Code -Excited Linear Prediction. Annex B: A Silence Compression Scheme for G.729 Optimized for Terminals Conforming to Recommend. V.70, International Telecommunication Union, 1996.
[109]Stegmann J , Schroder G . Robust voice-activity detection based on the wavelet transform[C]// IEEE Workshop on Speech Coding for Telecommunications Proceeding. IEEE, 1997.
[110]Itoh, K.; Mizushima, M. Environmental noise reduction based on speech/non-speech identification for hearing aids. In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, Munich, Germany, 21–24 April 1997; pp. 419–422.
[111]R. Sarikaya and J. H. L. Hansen, “Robust speech activity detection in the presence of noise,” in Proc. 5th Int. Conf. Spoken Language Processing,1997, pp. 922–925.
[112]Adaptive Multi Rate (AMR) Speech; ANSI-C code for AMR Speech Codec, 1998.
[113]Digital Cellular Telecommunications System (Phase 2+); Adaptive Multi Rate (AMR); Speech Processing Functions; General Description,1998
[114]J. Sohn and W. Sung, “A voice activity detector employing soft decision based noise spectrum adaptation,” in Proc. IEEE ICASSP’98, vol.1, Seattle, WA, 1998, pp. 365–368.
[115]Sohn J , Kim N S , Sung W . A statistical model-based voice activity detection[J]. IEEE Signal Processing Letters, 1999, 6(1):1-3.
[116]Malah D . System and method for noise threshold adaptation for voice activity detection in non-stationary noise environments[J]. Journal of the Acoustical Society of America, 2000, 108(3):885.
[117]Press E . Method and device for voice activity detection and a communication device[J]. Journal of the Acoustical Society of America, 2000, 108(1):21.
[118]Mekuria F . Non-parametric voice activity detection: US 2000.
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