• DocumentCode
    1648097
  • Title

    A novel recurrent network based pitch detection technique for quasi-periodic/pitch-varying signals

  • Author

    Chang, Wei-Chen ; Su, Alvin W Y

  • Author_Institution
    Dept. of Comput. Sci. & Ing. Eng., Nat. Cheng Kung Univ., Tainan, Taiwan
  • Volume
    1
  • fYear
    2002
  • fDate
    6/24/1905 12:00:00 AM
  • Firstpage
    816
  • Lastpage
    821
  • Abstract
    The accuracy of pitch detection algorithms affects the performance of many speech and audio applications such as speech compression, computer music analysis/synthesis and information retrieval of audio signals. In many applications, it is also desired that the algorithms should be robust to background noise. A recurrent network based method is proposed in this paper. Though the proposed method requires more computation compared to some existing methods, it is more accurate and less sensitive to noise. The other advantage is that it requires a smaller time frame to estimate the pitch compared to other methods. Therefore, it is more suitable for tracking the pitch of a pitch-varying signal or a quasi-periodic signal. Both the synthesized and natural tones are used in the computer simulation
  • Keywords
    function approximation; learning (artificial intelligence); recurrent neural nets; speech processing; speech recognition; audio signals; background noise reduction; function approximation; learning algorithm; pitch detection; quasi-periodic signal; recurrent neural network; speech recognition; Algorithm design and analysis; Application software; Detection algorithms; Information analysis; Music; Performance analysis; Signal analysis; Signal synthesis; Speech analysis; Speech synthesis;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
  • Conference_Location
    Honolulu, HI
  • ISSN
    1098-7576
  • Print_ISBN
    0-7803-7278-6
  • Type

    conf

  • DOI
    10.1109/IJCNN.2002.1005579
  • Filename
    1005579