Voiced/Unvoiced Classification and Pitch Estimation Based on Amplitude Compression Filter

XU Jingyun; ZHAO Xiaoqun; WANG Qiao; WANG Digang

doi:10.11999/JEIT150778

Volume 38 Issue 3

Mar. 2016

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Article Navigation > Journal of Electronics & Information Technology > 2016 > 38(3): 586-593

XU Jingyun, ZHAO Xiaoqun, WANG Qiao, WANG Digang. Voiced/Unvoiced Classification and Pitch Estimation Based on Amplitude Compression Filter[J]. Journal of Electronics & Information Technology, 2016, 38(3): 586-593. doi: 10.11999/JEIT150778

Citation:

XU Jingyun, ZHAO Xiaoqun, WANG Qiao, WANG Digang. Voiced/Unvoiced Classification and Pitch Estimation Based on Amplitude Compression Filter[J]. Journal of Electronics & Information Technology, 2016, 38(3): 586-593. doi: 10.11999/JEIT150778

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Voiced/Unvoiced Classification and Pitch Estimation Based on Amplitude Compression Filter

doi: 10.11999/JEIT150778

1.
2.
(School of Electronics and Information, Tongji University, Shanghai 201804, China)

Funds:

The National Natural Science Foundation of China (61271248), The Natural Science Foundation of Huzhou City (2015YZ04)

Received Date: 2015-06-29
Rev Recd Date: 2015-12-02
Publish Date: 2016-03-19

Abstract

Abstract

A method of voiced/unvoiced classification and pitch estimation based on Pitch Estimation Filter with Amplitude Compression (PEFAC) is proposed in this paper. The method first attenuates strong noise components at the?low frequencies based on PEFAC and extracts pitch harmonic from noisy speech in the log-frequency domain. Then, the harmonic number associated with the pitch harmonic is determined by Symmetric average magnitude sum function weighted Impulse-train Matching (SIM) scheme in time domain. A pitch tracking scheme using dynamic programming is applied to select the pitch candidates and a voiced speech probability is computed from the likelihood ratio of Gaussian Mixture Models (GMMs) classifiers based on 3-element feature vector. The simulated results show that the proposed method efficiently reduces voiced/unvoiced and pitch estimation error, and it is superior to some of the state-of-theart method in the real environment.

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References(24)

References

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