Novel DTD and VAD assisted voice detection algorithm for VoIP systems

doi:10.1016/S1005-8885(16)60040-7

中国邮电高校学报(英文) ›› 2016, Vol. 23 ›› Issue (4): 9-16.doi: 10.1016/S1005-8885(16)60040-7

Novel DTD and VAD assisted voice detection algorithm for VoIP systems

明萌¹,王珂¹,纪红²

1. 北京邮电大学
2. 北邮199信箱

收稿日期:2016-03-14 修回日期:2016-05-24 出版日期:2016-08-30 发布日期:2016-08-30
通讯作者: 明萌 E-mail:mingmeng2013@126.com

Novel DTD and VAD assisted voice detection algorithm for VoIP systems

Meng MING¹ Wang-Ke ¹Ji-Hong²

Received:2016-03-14 Revised:2016-05-24 Online:2016-08-30 Published:2016-08-30
Contact: Meng MING E-mail:mingmeng2013@126.com

摘要/Abstract

摘要： Echo cancellation plays an important role in current Internet protocol (IP) based voice interactive systems. Voice state detection is an essential part in echo cancellation. It mainly comprises two parts: double talk detection (DTD) and voice activity detection (VAD). DTD is used to detect doubletalk and prevent filter divergence in the presence of near-end speech, and VAD is used to determine the near-end voice activity and output silence indicator when near-end is silent. However, DTD straightforwardly proceeded may mistakenly declare double talk under double silent condition, coefficients update under the far-end silence condition may lead to filter divergence, and current VAD algorithms may misjudge the residual echo from the near end to be far-end voice. Therefore, a voice detection algorithm combining DTD and far-end VAD is proposed. DTD is implemented when VAD declares far-end speech, filtering and coefficients update will be halted when VAD declares far-end silence, and the far-end VAD adopted is multi-feature VAD based on short-time energy and correlation. The new algorithm can improve the accuracy of DTD, prevent filter divergence, and exclude the circumstance that far-end signal only contains residual echo from near end. Actual test results show that the voice state decision of the new algorithm is accurate, and the performance of echo cancellation is improved.

关键词: echo cancellation, double talk detection (DTD), voice activity detection (VAD), adaptive filter

Abstract: Echo cancellation plays an important role in current Internet protocol (IP) based voice interactive systems. Voice state detection is an essential part in echo cancellation. It mainly comprises two parts: double talk detection (DTD) and voice activity detection (VAD). DTD is used to detect doubletalk and prevent filter divergence in the presence of near-end speech, and VAD is used to determine the near-end voice activity and output silence indicator when near-end is silent. However, DTD straightforwardly proceeded may mistakenly declare double talk under double silent condition, coefficients update under the far-end silence condition may lead to filter divergence, and current VAD algorithms may misjudge the residual echo from the near end to be far-end voice. Therefore, a voice detection algorithm combining DTD and far-end VAD is proposed. DTD is implemented when VAD declares far-end speech, filtering and coefficients update will be halted when VAD declares far-end silence, and the far-end VAD adopted is multi-feature VAD based on short-time energy and correlation. The new algorithm can improve the accuracy of DTD, prevent filter divergence, and exclude the circumstance that far-end signal only contains residual echo from near end. Actual test results show that the voice state decision of the new algorithm is accurate, and the performance of echo cancellation is improved.

Key words: echo cancellation, double talk detection (DTD), voice activity detection (VAD), adaptive filter

中图分类号:

TN912.3

Meng MING Wang-Ke Ji-Hong. Novel DTD and VAD assisted voice detection algorithm for VoIP systems[J]. JOURNAL OF CHINA UNIVERSITIES OF POSTS AND TELECOM, 2016, 23(4): 9-16.

参考文献

1. Yu L. Low computational complexity double-talk detection algorithm. Computer Engineering and Applications, 2011, 47(16): 147?150 (in Chinese) 2. Liu Y Y. Studies and improvement on echo cancellation algorithm based on NLMS. Master Thesis. Xi’an, China: Xidian University, 2006 (in Chinese) 3. Marzinzik M, Kollmeier B. Speech pause detection for noise spectrum estimation by tracking power envelope dynamics. IEEE Transactions on Speech and Audio Processing, 2002, 10(2): 109?118 4. Cho J H, Morgan D R, Benesty J. An objective technique for evaluating doubletalk detectors in acoustic echo cancelers. IEEE Transactions on Speech and Audio Processing, 1999, 7(6): 718?724 5. Ye H, Wu B X. A new double-talk detection algorithm based on the orthogonality theorem. IEEE Transactions on Communications, 1991, 39(11): 1542?1545 6. Sonika, Dhull S. Double talk detection in acoustic echo cancellation based on variance impulse response. International Journal of Electronics and Communication Engineering, 2011, 4(5): 537?542 7. Park Y S, Song J H, Kang S I, et al. A statistical model-based double-talk detection incorporating soft decision. Proceedings of the 2010 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP’10), Mar 14?19, 2010, Dallas, TX, USA. Piscataway, NJ, USA: IEEE, 2010: 5082?5085 8. Paleologu C, Benesty J, Gaensler T, et al. Class of double-talk detectors based on the holder inequality. Proceedings of the 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP’11), May 22?27, 2011, Prague, Czech. Piscataway, NJ, USA: IEEE, 2011: 425?428 9. Ikram M Z. Double-talk detection in acoustic echo cancellers using zero-crossings rate. Proceedings of the 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP’15), Apr 19?24, 2015, South Brisbane, Australia. Piscataway, NJ, USA: IEEE, 2015: 1121?1125 10. Hamidia M, Amrouche A. Double-talk detection using the singular value decomposition for acoustic echo cancellation. Proceedings of the 2013 IEEE International Conference on Communications (ICC’13), Jun 9?13, 2013, Budapest, Hungary. Piscataway, NJ, USA: IEEE, 2013: 4745?4749 11. Bao H B, Yang Y, Liu J, et al. A robust algorithm of double talk detection based on voice activity detection. Proceedings of the 2010 IEEE International Conference on Audio Language and Image Processing (ICALIP’10), Nov 23?25, 2010, Shanghai, China. Piscataway, NJ, USA: IEEE, 2010: 12?15 12. Dong E Q, Liu G Z, Zhou Y T, et al. Voice activity detection based on short-time energy and noise spectrum adaptation. Proceedings of the 6th International Conference on Signal Processing: Vol 1, Aug 26?30, 2002, Beijing, China. Piscataway, NJ, USA: IEEE, 2002: 464?467 13. Xi D L, Li R W, Chen H L. A speech endpoint detection algorithm based on maximum of auto-correlation function and amended threshold-crossing rate. Audio Engineering, 2010, 34(4): 53?57,66 (in Chinese) 14. Liu S H, Hu Q, Qin T F, et al. A method of the voice endpoint detection based on maximum of autocorrelation function. Audio Engineering, 2006, 30(12): 47?50 (in Chinese) 15. Tucker R. Voice activity detection using a periodicity measure. IEE Proceedings I: Communications, Speech and Vision, 1992, 139(4): 377?380 16. Davis A, Nordholm S, Togneri R. Statistical voice activity detection using low-variance spectrum estimation and an adaptive threshold. IEEE Transactions on Audio, Speech, and Language Processing, 2006, 14(2): 412?424 17. Nemer E, Goubran R, Mahmoud S. Robust voice activity detection using higher-order statistics in the LPC residual domain. IEEE Transactions on Speech and Audio Processing, 2001, 9(3): 217?231 18. Yang X L, Tan B H, Ding J H, et al. Comparative study on voice activity detection algorithm. Proceedings of the 2010 International Conference on Electrical and Control Engineering (ICECE’10), Jun 25?27, 2010, Wuhan, China. Piscataway, NJ, USA: IEEE, 2010: 599?602

[1]	Xu Qinyu, Lu Guanming, Yan Jingjie, Li Haibo, Cheng Xiao. Speaker conversion using kernel non-negative matrix factorization[J]. 中国邮电高校学报(英文版), 2017, 24(5): 60-67.
[2]	赵剑董远赵贤宇杨浩王海拉. Cross similarity measurement for speaker adaptive test normalization in text-independent speaker verification[J]. Acta Metallurgica Sinica(English letters), 2008, 15(2): 130-134.
[3]	邓宗元;劭羲;杨震. LSB steganalysis of speech data based on distance measure and ML decision[J]. Acta Metallurgica Sinica(English letters), 2007, 14(3): 103-107.
[4]	YING Na, ZHAO Xiao-hui, DONG Jing. Unvoiced/voiced classification and voiced harmonic parameters estimation using the third-order statistics[J]. Acta Metallurgica Sinica(English letters), 2007, 14(1): 85-89.

Novel DTD and VAD assisted voice detection algorithm for VoIP systems

Novel DTD and VAD assisted voice detection algorithm for VoIP systems

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 4

编辑推荐

Metrics

本文评价