TY - JOUR
T1 - A multichannel recurrent network analysis/synthesis model for coupled-string instruments
AU - Chang, Wei Chen
AU - Su, Alwin W.Y.
N1 - Funding Information:
Manuscript received January 20, 2005; revised October 6, 2005. This work was supported in part by National Science Committee, Taiwan, R.O.C., under Contract 93-2213-E-006-025. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Hong-Goo Kang.
PY - 2006/11
Y1 - 2006/11
N2 - 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
AB - 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
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U2 - 10.1109/TASL.2006.872610
DO - 10.1109/TASL.2006.872610
M3 - Article
AN - SCOPUS:64549163611
VL - 14
SP - 2233
EP - 2241
JO - IEEE Transactions on Speech and Audio Processing
JF - IEEE Transactions on Speech and Audio Processing
SN - 1558-7916
IS - 6
M1 - 1709910
ER -