• DocumentCode
    788528
  • Title

    A Multichannel Recurrent Network Analysis/Synthesis Model for Coupled-String Instruments

  • Author

    Chang, Wei-Chen ; Su, Alvin W Y

  • Author_Institution
    Dept. of Comput. Sci. & Inf. Eng., Nat. Cheng Kung Univ., Tainan
  • Volume
    14
  • Issue
    6
  • fYear
    2006
  • Firstpage
    2233
  • Lastpage
    2241
  • Abstract
    Struck-string instruments such as pianos usually have groups of strings that terminate at some common bridges. Because of the strong coupling phenomenon, the produced tones can exhibit highly complex modulation patterns, and synthesizing such complex tones turns out to be quite complicated. It is also difficult to determine the synthesis model parameters such that the synthesized tones can match the recorded tones. This paper proposes a multichannel recurrent network based on three previous works: the coupled-string model, the commuted piano synthesis method, and the infinite impulse response (IIR) synthesis method. This work attempts to automatically extract the synthesis parameters by using a neural-network training algorithm without the knowledge of the instruments´ physical properties. Encouraging results are shown in the computer simulations
  • Keywords
    electrical engineering computing; electronic music; modulation; musical instruments; recurrent neural nets; commuted piano synthesis method; complex modulation patterns; coupled-string instruments; infinite impulse response; multichannel recurrent network analysis; synthesis model; synthesis model parameters; Amplitude modulation; Bridges; Computer simulation; Digital filters; Instruments; Motion analysis; Network synthesis; Parameter estimation; Recurrent neural networks; Signal synthesis; Coupling phenomenon; parameter estimation; recurrent neural networks; sound synthesis;
  • fLanguage
    English
  • Journal_Title
    Audio, Speech, and Language Processing, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1558-7916
  • Type

    jour

  • DOI
    10.1109/TASL.2006.872610
  • Filename
    1709910