Bootstrap

语音端点检测（voice activity detection VAD）综述+论文百篇（195*~2024）

能量

短时过零率

自相关

pitch

G.729B

AMR opt 1/2

深度学习

bDNN

基于听觉机制

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]

Pitch, MFCC, LPC, PLP phase, and spectra-gram.

Deep learning

Noisy / unstable noise

更详细的论文和相关代码请参考（欢迎各位star呀，有啥问题可以直接留言）：GitHub - linan2/Voice-activity-detection-VAD-paper-and-code: Voice activity detection (VAD) paper（From 198*~ ）and its classification. The arrangement of these papers was arranged when I was studying for a double master degree in UNOKI LAB of JAIST. Now share it with those in need to learn.

  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
  22. Nemer E . Robust voice activity detection using higher-order statistics in the LPC residual domain[J]. IEEE Transactions On Speech And Audio Processing, 2001, 9(3):217-231.
  23. P. Renevey and A. Drygajlo, “Entropy based voice activity detection in very noisy conditions,” Proc. Eurospeech, pp. 1887–1890, Sep. 2001.
  24. Beritelli, F.; Casale, S.; Ruggeri, G.; Serano, S. Performance Evaluation and Comparison of G.729/AMR/Fuzzy Voice Activity Detectors. IEEE Signal Process. Let. 2002,9, 85–88
  25. Tanyer S G , Ozer H . Voice activity detection in nonstationary noise[J]. IEEE Transactions on Speech and Audio Processing, 2002, 8(4):478-482.
  26. Sangwan, A.; Chiranth, M.C.; Jamadagni, H.S.; Sah, R.; Venkatesha Prasad, R.; Gaurav, V. VAD techniques for real-time speech transmission on the Internet. In Proceedings of the 5th IEEE International Conference on High Speed Networks and Multimedia Communications, Jeju Island, Korea, 3–5 July 2002; pp. 46–50
  27. Dong E, Liu G, Zhou Y, et al. Applying Support Vector Machines to Voice Activity Detection[J]. 2003.
  28. Tian Y , Wu J , Wang Z , et al. Fuzzy clustering and Bayesian information criterion based threshold estimation for robust voice activity detection.[C]// IEEE International Conference on Acoustics. IEEE, 2003.
  29. Joon-Hyuk Chang, Nam Soo Kim, "Voice activity detection based on complex laplacian model", Electron. Lett., vol. 39, no. 7, pp. 632-634, 2003
  30. Ramirez, J.; Segura, J.C.; Benitez, C.; Torre, A.; Rubio, A. Efficient voice activity algorithms using long-term speech information. Speech Commun. 2004,42, 271–287.
  31. Li Y, Zhang R, Cui H, et al. Voice activity detection algorithm with low signal-to-noise ratios based on the spectrum entropy[J]. Journal of Tsinghua University, 2005.
  32. 5. J. Ramírez, J. C. Segura, C. Benítez, L. García, A. Rubio, "Statistical voice activity detection using a multiple observation likelihood ratio test", IEEE Signal Process. Lett., vol. 12, no. 10, pp. 689-692, 2005.
  33. Jaume Padrell, Dusan Macho, Climent Nadeu, "Robust speech activity detection using lda applied to ff parameters", Proc. ICASSP, vol. 1, pp. I-557, 2005.
  34. Zhang, L.; Gao, Y.-C.; Bian, Z.-Z.; Chen, L. Voice activity detection algorithm improvement in multi-rate speech coding of 3GPP. In Proceedings of the 2005 International Conference on Wireless Communications,Networking and Mobile Computing, (WCNM 2005), Wuhan, China, 23–26 September 2005; pp. 1257–1260.
  35. Kristjansson, T.; Deligne, S.; Olsen, P.A. Voicing features for robust speech detection. In Proceedings of the Ninth European Conference on Speech Communication and Technology, Lisbon, Portugal, 4–8 September 2005; pp. 369–372.
  36. Davis, A.; Nordholm, S.; Togneri, R. Statistical Voice Activity Detection Using Low-Variance Spectrum Estimation and an Adaptive Threshold. IEEE Trans. Audio Speech Lang. Process. 2006,14, 412–424.
  37. Lee, Y.-C.; Ahn, S.-S. Statistical Model-Based VAD algorithm with wavelet transform. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. 2006,E89-A, 1594–1600
  38. J. H. Chang, N. S. Kim, and S. K. Mitra, “Voice activity detection based on multiple statistical models,” IEEE Trans. Signal Process., vol. 54, no. 6, pp. 1965–1976, Jun. 2006.
  39. Li X , Liu H , Zheng Y , et al. Robust Speech Endpoint Detection Based on Improved Adaptive Band-Partitioning Spectral Entropy[M]// Bio-Inspired Computational Intelligence and Applications. Springer Berlin Heidelberg, 2007.
  40.  J. Ramírez, J. Segura, J. Górriz, and L. García, “Improved voice activity detection using contextual multiple hypothesis testing for robust speech recognition,” IEEE Trans. Audio, Speech, Lang. Process., vol. 15, no. 8, pp. 2177–2189, Nov. 2007.
  41. R. Tahmasbi and S. Rezaei, “A soft voice activity detection using GARCH filter and variance Gamma distribution,” IEEE Trans. Audio, Speech, Lang. Process., vol. 15, no. 4, pp. 1129–1134, May 2007.
  42. Sakai H , Cincarek T , Kawanami H , et al. Voice Activity Detection Applied to Hands-Free Spoken Dialogue Robot based on Decoding using Acoustic and Language Model[C]// International Icst Conference on Robot Communication & Coordination. 2007.
  43. Sakai H , Cincarek T , Kawanami H , et al. Voice Activity Detection Applied to Hands-Free Spoken Dialogue Robot based on Decoding using Acoustic and Language Model[C]// International Icst Conference on Robot Communication & Coordination. 2007.
  44. T. Kinnunen, E. Chermenko, M. Tuononen, P. Franti and H. Li, Voice activity detection using mfcc features and support vector machine, Proc. Speech and Computer 2 (2007), 556–561.
  45. D.G. Ha, S.J. Cho, G.G. Jin and O.K, Shin, Voice activity detection based on signal energy and entropy-difference in noisy environments, Journal of the Korean Society of Marine Engineering 32 (2008), 768–774.
  46. M. Asgari, A. Sayadian, M. Farhadloo and E.A. Mehrizi, Voice activity detection using entropy in spectrum domain, Proc. Telecommunication Networks and Applications Conference, 2008, 407–410.
  47. S. I. Kang, Q. H. Jo, and J. H. Chang, “Discriminative weight training for a statistical model-based voice activity detection,” IEEE Signal Process. Lett., vol. 15, pp. 170–173, 2008.
  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.
  50. Huang H , Lin F . A speech feature extraction method using complexity measure for voice activity detection in WGN[J]. Speech Communication, 2009, 51(9):714-723.
  51. Moattar M H, Homayounpour M M . A simple but efficient real-time Voice Activity Detection algorithm[C]// European Signal Processing Conference. IEEE, 2009.
  52. Yoo I C , Yook D . Robust Voice Activity Detection Using the Spectral Peaks of Vowel Sounds[J]. ETRI Journal, 2009, 31(4):451-453.
  53. Yoo I C , Yook D . Robust Voice Activity Detection Using the Spectral Peaks of Vowel Sounds[J]. ETRI Journal, 2009, 31(4):451-453.
  54. Asgari M , Sayadian A , Tehranipour F , et al. Novel Voice Activity Detection Based on Vector Quantization[C]// International Conference on Computer Modelling & Simulation. IEEE, 2009.
  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.
  58. Ghosh P K , Tsiartas A , Narayanan S . Robust Voice Activity Detection Using Long-Term Signal Variability[J]. IEEE Transactions on Audio, Speech, and Language Processing, 2010, 19(3):600-613.
  59. Fukuda T , Ichikawa O , Nishimura M . Long-Term Spectro-Temporal and Static Harmonic Features for Voice Activity Detection[J]. IEEE Journal of Selected Topics in Signal Processing, 2010, 4(5):834-844.
  60. L. N. Tan, B. J. Borgstrom, and A. Alwan, “Voice activity detection using harmonic frequency components in likelihood ratio test,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process., Mar. 2010, pp. 4466–4469.
  61. J. W. Shin, J. H. Chang, and N. S. Kim, “Voice activity detection based on statistical models and machine learning approaches,” Comput. Speech Lang., vol. 24, no. 3, pp. 515–530, 2010.
  62. N.Dhananjaya and B.Yegnanarayana, “Voiced/nonvoiced detection based on robustness of voiced epochs,” IEEE Signal Process. Lett., vol. 17, no. 3, pp. 273–276, Mar. 2010.
  63. Ishizuka, K.; Nakatani, T.; Fujimoto, M.; Miyazaki, N. Noise robust voice activity detection based on periodic to aperiodic component ratio. Speech Commun. 2010,52, 41–60.
  64. H. Ghaemmaghami, B. J. Baker, R. J. Vogt, and S. Sridharan, “Noise robust voice activity detection using features extracted from the time domain auto-correlation function,” in Proc. Interspeech, Makuhari, Japan, 2010, pp. 3118–3121.
  65. K. Ishizuka, T. Nakatani, M. Fujimoto, and N. Miyazaki, “Noise robust voice activity detection based on periodic to aperiodic component ratio,” Speech Commun., vol. 52, no. 1, pp. 41–60, Jan. 2010.
  66. Liu B S, Lu Z M, Shen L R , et al. Voice activity detection with low signal-to-noise ratio based on Hilbert-Huang transform[J]. Journal of Jilin University (Engineering and Technology Edition), 2011, 41(3):844-848.
  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.
  73. Peng Teng, Yunde Jia, "Voice activity detection via noise reducing using non-negative sparse coding", IEEE Signal Process. Lett., vol. 20, no. 5, pp. 475-478, 2013.
  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.
  81. S. O. Sadjadi and J. H. Hansen, “Unsupervised speech activity detection using voicing measures and perceptual spectral flux,” IEEE Signal Process. Lett., vol. 20, no. 3, pp. 197–200, Mar. 2013.
  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.

  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,” in Proc. IEEE ICASSP’98, vol.1, Seattle, WA, 1998, pp. 365–368.
  13. Sohn J , Kim N S , Sung W . A statistical model-based voice activity detection[J]. IEEE Signal Processing Letters, 1999, 6(1):1-3.
  14. 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.
  15. 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.
  16. Mekuria F . Non-parametric voice activity detection: US 2000.
  17. 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.
  18. 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.
  19. 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
  20. P. Renevey and A. Drygajlo, “Entropy based voice activity detection in very noisy conditions,” Proc. Eurospeech, pp. 1887–1890, Sep. 2001.
  21. Beritelli, F.; Casale, S.; Ruggeri, G.; Serano, S. Performance Evaluation and Comparison of G.729/AMR/Fuzzy Voice Activity Detectors. IEEE Signal Process. Let. 2002,9, 85–88
  22. Tanyer S G , Ozer H . Voice activity detection in nonstationary noise[J]. IEEE Transactions on Speech and Audio Processing, 2002, 8(4):478-482.
  23. Sangwan, A.; Chiranth, M.C.; Jamadagni, H.S.; Sah, R.; Venkatesha Prasad, R.; Gaurav, V. VAD techniques for real-time speech transmission on the Internet. In Proceedings of the 5th IEEE International Conference on High Speed Networks and Multimedia Communications, Jeju Island, Korea, 3–5 July 2002; pp. 46–50
  24. Dong E, Liu G, Zhou Y, et al. Applying Support Vector Machines to Voice Activity Detection[J]. 2003.
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[1] Li N ,  Wang L ,  Unoki M , et al. Robust voice activity detection using a masked auditory encoder based convolutional neural network[C]// In Proc. IEEE-ICASSP, 2021. IEEE, 2021.

基于能量和短时过零率的语音增强A simple VAD method. Contribute to linan2/VAD_MATLAB development by creating an account on GitHub.icon-default.png?t=N7T8https://github.com/linan2/VAD_MATLAB.git

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