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3G系统中复杂背景噪声环境下话音激活检测算法性能分析 被引量:1

Performance Analysis of VAD Algorithm Applied for 3G System in Complex Background Noise Environment
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摘要 以第三代移动通信合作项目组 ( 3GPP)和欧洲电信标准委员会 ( ETSI) GSM相关标准为依据 ,分析自适应多速率语音编码器中话音激活检测算法在不同背景噪声和不同输入信噪比下的性能 .基于对大量实验结果的分析 ,对其在 3G系统中应用的鲁棒性加以验证 ,同时和国际电联电信标准组 ( ITU- T)建议的 G.72 9算法进行比较 .研究表明 ,该算法在非平稳背景噪声环境下的性能优于 G.72 9,在保证重构语音足够高可懂度和自然度的前提下 ,使整个移动通信系统容量增加约 30 % . In terms of standards recommended by 3GPP and ETSI, the performance of voice activity detection (VAD) algorithm applied for adaptive multi rate(AMR) speech codec was simulated and analyzed with varying background noise and different signal to noise ratios. Based on a great deal of test results, the robustness of AMR VAD applied for 3G system was verified and compared with ITU G.729 VAD. It proves that the AMR VAD performs better than G.729 VAD in nonstationary background noise environment, the capacity of mobile communication system is expanded about 30% when the intelligibility and naturalness of reconstructed speech is not impaired.
作者 陈东 匡镜明
出处 《北京理工大学学报》 EI CAS CSCD 北大核心 2001年第2期232-236,共5页 Transactions of Beijing Institute of Technology
基金 国际合作项目
关键词 话音激活检测 自适应多速率 间断传输 GSM 3G系统 voice activity detection adaptive multi rate discontinuous transmission
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参考文献4

  • 1[1]ETSI GSM 06.90, Digital cellular telecommunications system (Phase 2+): Adaptive multi-rate (AMR)[S].
  • 2[2]ITU-T G.729 Annex B, A silence compression scheme for G.729 optimized for terminals conforming to recommendation V.70[S].
  • 3[3]3GPP TS 26.94 V3.0.0, AMR speech CODEC: Voice activity detection[S].
  • 4[4]Freeman D K, Cosier G, Southcott C B, et al. The voice activity detector for the Pan-European digital cellular mobile telephone service[Z] . ICASSP, ′89, Glasgow, 1989.

同被引文献8

  • 1Ying D, Yan Y, Dang J, et al. Voice activity detection based on an unsupervised learning framework[J]. IEEE Trans on Audio, Speech, Lang. Process, 2011,19(8):2624-2633.
  • 2Zhang Xiaolei, Wu Ji. Deep belief networks based voice activity detection[J]. IEEE Trans on Speech Audio Process, 2013,21(4):697-710.
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  • 4Ying D, Yan Y. Noise estimation using a constrained sequential hidden Markov model in the log-spectral domain[J]. IEEE Trans. on Audio, Speech, Lang. Process, 2013,21(6):1145-1157.
  • 5Shi Yu, Soong F K, Zhon Jianlai. Auto-segmentation based partitioning and clustering approach to robust and pointing[C]//Proceedings of International Conference on Acoustics, Speech and Signal Processing. Toulouse, France:[s.n.], 2006:793-796.
  • 6Shi Yu, Soong F K, Zhou Jianlai. Auto-segmentation based VAD for robust ASR[C]//Proceedings of Interspeech. Pittsburgh, USA:[s.n.], 2006:1958-1961.
  • 7李正友,李天伟,黄谦,隋振庚.噪声环境中的汉语浊语音检测[J].声学学报,2014,39(4):517-522. 被引量:1
  • 8强勇,焦李成,保铮.动态规划算法进行弱目标检测的机理研究[J].电子与信息学报,2003,25(6):721-727. 被引量:46

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