• DocumentCode
    1299722
  • Title

    Model-based synthesis of plucked string instruments by using a class of scattering recurrent networks

  • Author

    Liang, Sheng-Fu ; Su, Alvin W Y ; Lin, Chin-Teng

  • Author_Institution
    Dept. of Electr. & Control Eng., Nat. Chiao Tung Univ., Hsinchu, Taiwan
  • Volume
    11
  • Issue
    1
  • fYear
    2000
  • fDate
    1/1/2000 12:00:00 AM
  • Firstpage
    171
  • Lastpage
    185
  • Abstract
    A physical modeling method for electronic music synthesis of plucked-string tones by using recurrent networks is proposed. A scattering recurrent network (SRN) which is used to analyze string dynamics is built based on the physics of acoustic strings. The measured vibration of a plucked string is employed as the training data for the supervised learning of the SRN. After the network is well trained, it can be regarded as the virtual model for the measured string and used to generate tones which can be very close to those generated by its acoustic counterpart. The “virtual string” corresponding to the SRN can respond to different “plucks” just like a real string, which is impossible using traditional synthesis techniques such as frequency modulation and wavetable. The simulation of modeling a cello “A”-string demonstrates some encouraging results of the new music synthesis technique. Some aspects of modeling and synthesis procedures are also discussed
  • Keywords
    electronic music; frequency modulation; learning (artificial intelligence); musical instruments; recurrent neural nets; acoustic strings; electronic music synthesis; measured vibration; model-based synthesis; physical modeling method; plucked string instruments; plucked-string tones; scattering recurrent networks; string dynamics; supervised learning; virtual model; Acoustic measurements; Acoustic scattering; Electronic music; Frequency modulation; Instruments; Network synthesis; Physics; Supervised learning; Training data; Vibration measurement;
  • fLanguage
    English
  • Journal_Title
    Neural Networks, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1045-9227
  • Type

    jour

  • DOI
    10.1109/72.822519
  • Filename
    822519