Browsing by Author "Su, AWY"

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A class of physical modeling recurrent networks for analysis/synthesis of plucked string instruments
(2002-09-01)
Su, AWY
;
Liang, SF
;
電控工程研究所
;
Institute of Electrical and Control Engineering
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.
Model-based synthesis of plucked string instruments by using a class of scattering recurrent networks
(2000-01-01)
Liang, SF
;
Su, AWY
;
Lin, CT
;
電控工程研究所
;
Institute of Electrical and Control Engineering
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.
Modeling and analysis of acoustic musical strings using Kelly-Lochbaum lattice networks
(2004-11-01)
Liang, SF
;
Su, AWY
;
電控工程研究所
;
Institute of Electrical and Control Engineering
Musical strings play important roles in the timbre of a stringed instrument. An instrument installed with different strings can produce very different sounds. To model a plucked-string instrument physically, analyzing its strings may be a good starting point. In this paper, a novel strategy for the modeling and analysis of acoustic musical strings is proposed. In order to obtain the vibration of an acoustic string, a measurement device with multiple sets of electromagnetic pickups is built. A recurrent network based on the Kelly-Lochbaum structure with multiple sets of system parameters is proposed to simulate the time-varying vibrating behavior of a target string. A fast training algorithm combining BPTT and SARPROP is also proposed to train the network automatically. The well-trained recurrent network based model can be regarded as a close simulation model of a real string, and using the model, it becomes easier to understand the characteristics of a string under different conditions when it vibrates.
Recurrent neural-network-based physical model for the chin and other plucked-string instruments
(2000-11-01)
Liang, SF
;
Su, AWY
;
電控工程研究所
;
Institute of Electrical and Control Engineering
A new physical model with neural networks is presented. The structure of the network is designed for the analysis of plucked-string instruments, and this network is also used as the corresponding synthesis engine. The proposed approach also provides a general and automatic way of determining suitable synthesis parameters by using a supervised neural network training algorithm with recorded sounds of a specific played instrument as the training vector. This is a general method and can be used for any plucked-string instrument. A traditional Chinese plucked-string instrument, called the Chin, is used as the target instrument to demonstrate this new synthesis method.