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This paper contains a description of a machine-learning-based system for recognition and prediction of music. The presented system uses advanced data-mining algorithms: neural networks and rough-sets. The system was applied for two main purposes: recognition of musical: structures (phrase, rhythm and harmony) and for the prediction of musical elements (melody, rhythm and harmony). The system was optimized for each of the purposes. The problems related to the optimization process are presented. Conclusions concerning application of the machine learning methods to the music domain are derived and included.
Author (s): Szczerba, Marek;
Affiliation:
Technical University of Gdansk, Poland
(See document for exact affiliation information.)
AES Convention: 106
Paper Number:4904
Publication Date:
1999-05-06
Session subject:
Musical Acoustics
DOI:
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Szczerba, Marek; 1999; Recognition and Prediction of Music - A Machine Learning Approach [PDF]; Technical University of Gdansk, Poland; Paper 4904; Available from: https://aes.org/publications/elibrary-page/?id=8276
Szczerba, Marek; Recognition and Prediction of Music - A Machine Learning Approach [PDF]; Technical University of Gdansk, Poland; Paper 4904; 1999 Available: https://aes.org/publications/elibrary-page/?id=8276
@inproceedings{Szczerba1999recognition,
title={{Recognition and Prediction of Music - A Machine Learning Approach}},
author={Szczerba, Marek},
year={1999},
month={may},
booktitle={Journal of the Audio Engineering Society},
publisher={Paper 4904; AES Convention 106; May 1999},
number={4904},
organization={AES},
}
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