Machine Learning and Intelligent Communications. First International Conference, MLICOM 2016, Shanghai, China, August 27-28, 2016, Revised Selected Papers

Research Article

A Pitch Estimation Method Robust to High Levels of Noise

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  • @INPROCEEDINGS{10.1007/978-3-319-52730-7_28,
        author={Xu Jingyun and Zhao Xiaoqun and Cai Zhiduan},
        title={A Pitch Estimation Method Robust to High Levels of Noise},
        proceedings={Machine Learning and Intelligent Communications. First International Conference, MLICOM 2016, Shanghai, China, August 27-28, 2016, Revised Selected Papers},
        proceedings_a={MLICOM},
        year={2017},
        month={2},
        keywords={Pitch detection Pitch estimation filter with amplitude compression MCAMDF Dynamic programming},
        doi={10.1007/978-3-319-52730-7_28}
    }
    
  • Xu Jingyun
    Zhao Xiaoqun
    Cai Zhiduan
    Year: 2017
    A Pitch Estimation Method Robust to High Levels of Noise
    MLICOM
    Springer
    DOI: 10.1007/978-3-319-52730-7_28
Xu Jingyun,*, Zhao Xiaoqun1,*, Cai Zhiduan2,*
  • 1: Tongji University
  • 2: Huzhou University
*Contact email: xujingyunsh@gmail.com, Zhao_Xiaoqun@tongji.edu.cn, czddule@hutc.zj.cn

Abstract

Pitch is one of the most key parameter in speech coding, speech synthesis and so on, the traditional methods for pitch detection are prone to error at a low SNR at present. A pitch detection method based on pitch harmonic (PH) and the harmonic number based on PH is proposed in this paper. At first, the pitch harmonic is roughly estimated by pitch estimation filter with amplitude compression (PEFAC). Secondly, the weighted algorithm based on modified circular average magnitude difference function (MCAMDF) and pulse sequence is used to compute the pitch harmonic number. At last a pitch tracking method is applied to compute the pitch period candidates accurately. By simulation experiments, it is shown that the proposed pitch detection method has more accurate and more low algorithm complexity than the traditional methods at both high and low SNR.