A class of physical modeling recurrent networks for analysis/synthesis of plucked string instruments

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9 引文 斯高帕斯(Scopus)

摘要

A new approach is proposed that closely synthesizes tones of plucked string instruments by using a class of physical modeling recurrent networks. The strategies employed in this paper consist of a fast training algorithm and a multistage training procedure that are able to obtain the synthesis parameters for a specific instrument automatically. The training vector can be recorded tones of most target plucked instruments with ordinary microphones. The proposed approach delivers encouraging results when it is applied to different types of plucked string instruments such as steel-string guitar, nylon-string guitar, harp, Chin, Yueh-chin, and Pipa. The synthesized tones sound very close to the originals produced by their acoustic counterparts. In addition, this paper presents an embedded technique that can produce special effects such as vibrato and portamento that are vital to the playing of plucked-string instruments. The computation required in the resynthesis processing is also reasonable.

原文English
頁(從 - 到)1137-1148
頁數12
期刊IEEE Transactions on Neural Networks
13
發行號5
DOIs
出版狀態Published - 2002 9月

All Science Journal Classification (ASJC) codes

  • 軟體
  • 電腦科學應用
  • 電腦網路與通信
  • 人工智慧

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