语音端点检测（voice activity detection VAD）综述+论文百篇（195*~2019）

2023-11-23 07:30
文章标签 检测 detection activity 论文 2019 语音 综述 195 百篇 voice 端点 vad

本文主要是介绍语音端点检测（voice activity detection VAD）综述+论文百篇（195*~2019），希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

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

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  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
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  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
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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.https://github.com/linan2/VAD_MATLAB.git

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