Recurrent Neural-Network-Based Physical Model for the Chin and Other Plucked-String Instruments

Liang SF, Su AWY. Recurrent Neural-Network-Based Physical Model for the Chin and Other Plucked-String Instruments. J Audio Eng Soc. 2000;48(11):1045-1059. Available from: https://aes.org/publications/elibrary-page/?id=12037

@article{Liang2000_12037,
  author = {Liang, Sheng-Fu and Su, Alvin W. Y.},
  title = {{Recurrent Neural-Network-Based Physical Model for the Chin and Other Plucked-String Instruments}},
  journal = {Journal of the Audio Engineering Society},
  volume = {48},
  number = {11},
  pages = {1045--1059},
  year = {2000},
  month = nov,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=12037}
}

TY  - JOUR
TI  - Recurrent Neural-Network-Based Physical Model for the Chin and Other Plucked-String Instruments
AU  - Liang, Sheng-Fu
AU  - Su, Alvin W. Y.
T2  - Journal of the Audio Engineering Society
J2  - J. Audio Eng. Soc.
VL  - 48
IS  - 11
SP  - 1045
EP  - 1059
PY  - 2000
DA  - 2000/11/06
UR  - https://aes.org/publications/elibrary-page/?id=12037
PB  - Audio Engineering Society
LA  - en
AB  - 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.
ER  -