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

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2 Author(s)
A. W. Y. Su ; Dept. of CSIE, Nat. Cheng Kung Univ., Tainan, Taiwan ; Sheng-Fu Liang

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 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, the 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.

Published in:

IEEE Transactions on Neural Networks  (Volume:13 ,  Issue: 5 )