Classification of music by composer using fuzzy min-max neural networks. Sadeghian, P., Wilson, C., Goeddel, S., & Olmsted, A. In 2017 12th International Conference for Internet Technology and Secured Transactions (ICITST), pages 189–192, December, 2017. doi abstract bibtex This work utilizes high-level musical features extracted from a large music database of Sonata pieces composed by Beethoven, Corelli, and Mozart, and assesses the accuracy of Fuzzy Min-Max (FMM) Neural Network and Enhanced Fuzzy Min-Max (EFMM) Neural Network classifiers in classifying the classical pieces by composer. Results of the assessment are provided and show different accuracies depending on the parameters used in the FMM and EFMM models. This study presents a novel approach to the classification of music by composer by presenting two classifiers, namely FMM and EFMM Neural Networks, capable of classifying classical music by composer.
@inproceedings{sadeghian_classification_2017,
title = {Classification of music by composer using fuzzy min-max neural networks},
doi = {10.23919/ICITST.2017.8356375},
abstract = {This work utilizes high-level musical features extracted from a large music database of Sonata pieces composed by Beethoven, Corelli, and Mozart, and assesses the accuracy of Fuzzy Min-Max (FMM) Neural Network and Enhanced Fuzzy Min-Max (EFMM) Neural Network classifiers in classifying the classical pieces by composer. Results of the assessment are provided and show different accuracies depending on the parameters used in the FMM and EFMM models. This study presents a novel approach to the classification of music by composer by presenting two classifiers, namely FMM and EFMM Neural Networks, capable of classifying classical music by composer.},
booktitle = {2017 12th {International} {Conference} for {Internet} {Technology} and {Secured} {Transactions} ({ICITST})},
author = {Sadeghian, Pasha and Wilson, Casey and Goeddel, Stephen and Olmsted, Aspen},
month = dec,
year = {2017},
keywords = {Ridden},
pages = {189--192},
}
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