Application of Learning Algorithms to Musical Sound Analysis

Kostek B. Application of Learning Algorithms to Musical Sound Analysis. In: AES Convention 97. Audio Engineering Society; 1994. Paper 3873. Available from: https://aes.org/publications/elibrary-page/?id=6359

@inproceedings{Kostek1994_6359,
  author = {Kostek, Bozena},
  title = {{Application of Learning Algorithms to Musical Sound Analysis}},
  booktitle = {AES Convention 97},
  note = {Paper 3873},
  year = {1994},
  month = nov,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=6359}
}

TY  - CPAPER
TI  - Application of Learning Algorithms to Musical Sound Analysis
AU  - Kostek, Bozena
T2  - AES Convention 97
M1  - Paper 3873
PY  - 1994
DA  - 1994/11/06
UR  - https://aes.org/publications/elibrary-page/?id=6359
PB  - Audio Engineering Society
LA  - en
AB  - A novel approach to the computer analysis of musical sound features has been made applying learning algorithms to the assessment of subjective scaling factors. A rough set theory recognized in artificial intelligence proven to be especially interesting in applications to acoustical assessments. Foundations of this theory and basic principles underlying the rough set algorithms are shown. Some multidimensional scaling methods of musical timbre are reviewed in order to provide data for the rough set computations. Correspondingly, examples of automatic classifications of sound features are obtained. Conclusions concerning the artificial intelligence approach to the processing of acoustic data are included.
ER  -