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Xiao-Bing Zhang, Ting-Ting Sun, Yan-Ping Li. An Algorithm of Voice Activity Detection Based on EMD and Wavelet Entropy Ratio[J]. Journal of Electronic Science and Technology, 2017, 15(1): 64-68. DOI: 10.11989/JEST.1674-862X.6010715
Citation: Xiao-Bing Zhang, Ting-Ting Sun, Yan-Ping Li. An Algorithm of Voice Activity Detection Based on EMD and Wavelet Entropy Ratio[J]. Journal of Electronic Science and Technology, 2017, 15(1): 64-68. DOI: 10.11989/JEST.1674-862X.6010715
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An Algorithm of Voice Activity Detection Based on EMD and Wavelet Entropy Ratio

doi: 10.11989/JEST.1674-862X.6010715

  • School of Electrical and Information Engineering,Anhui University of Technology,Maanshan 243000,China

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This work was supported by the Significant Projects of Anhui University of Technology under Grant No.14206003

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  • Author Bio:

    Xiao-Bing Zhang. His current research is measurement and control technology,e-mail:zxb13013113592@126.com;
    Ting-Ting Sun. Her current interest is speech recognition,e-mail:609655209@qq.com;
    Yan-Ping Li. He is currently pursuing the M.S. degree with Anhui University of Technology. Her current interest is speech recognition,e-mail:1021754704@qq.com

    Xiao-Bing Zhang. His current research is measurement and control technology,e-mail:zxb13013113592@126.com;
    Ting-Ting Sun. Her current interest is speech recognition,e-mail:609655209@qq.com;
    Yan-Ping Li. He is currently pursuing the M.S. degree with Anhui University of Technology. Her current interest is speech recognition,e-mail:1021754704@qq.com

    Xiao-Bing Zhang. His current research is measurement and control technology,e-mail:zxb13013113592@126.com;
    Ting-Ting Sun. Her current interest is speech recognition,e-mail:609655209@qq.com;
    Yan-Ping Li. He is currently pursuing the M.S. degree with Anhui University of Technology. Her current interest is speech recognition,e-mail:1021754704@qq.com

  • Authors’ information: Xiao-Bing Zhang,e-mail:zxb13013113592@126.com
  • Received Date: 2016-01-06
  • Rev Recd Date: 2016-06-15
  • Publish Date: 2017-03-24
    Abstract
  • A new method was proposed to identify speech-segment endpoints based on the empirical mode decomposition (EMD) and a new wavelet entropy ratio with improving the accuracy of voice activity detection. With the EMD, the noise signals can be decomposed into several intrinsic mode functions (IMFs). Then the proposed wavelet energy entropy ratio can be used to extract the desired feature for each IMFs component. In view of the question that the method of voice endpoint detection based on the original wavelet entropy ratio cannot adapt to the low signal-to-noise ratio (SNR) condition, an appropriate positive constant was introduced to the basic wavelet energy entropy ratio with effectively improved discriminability between the speech and noise. After comparing the traditional wavelet energy entropy ratio with the proposed wavelet energy entropy ratio, the experiment results show that the proposed method is simple and fast. The speech endpoints can be accurately detected in low SNR environments.
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    • References(15)
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