Effective Training Methods for Automatic Musical Genre Classification

Eren Atsiz, Erinç Albey, Enis Kayış

2019

Abstract

Musical genres are labels created by human and based on mutual characteristics of songs, which are also called musical features. These features are key indicators for the content of the music. Rather than predictions by human decisions, developing an automatic solution for genre classification has been a significant issue over the last decade. In order to have automatic classification for songs, different approaches have been indicated by studying various datasets and part of songs. In this paper, we suggest an alternative genre classification method based on which part of songs have to be used to have a better accuracy level. Wide range of acoustic features are obtained at the end of the analysis and discussed whether using full versions or pieces of songs is better. Both alternatives are implemented and results are compared. The best accuracy level is 55% while considering the full version of songs. Besides, additional analysis for Turkish songs is also performed. All analysis, data, and results are visualized by a dynamic dashboard system, which is created specifically for the study.

Paper Citation

in Harvard Style

Atsiz E., Albey E. and Kayış E. (2019). Effective Training Methods for Automatic Musical Genre Classification.In Proceedings of the 8th International Conference on Data Science, Technology and Applications - Volume 1: DATA, ISBN 978-989-758-377-3, pages 275-280. DOI: 10.5220/0007933202750280

in Bibtex Style

@conference{data19,
author={Eren Atsiz and Erinç Albey and Enis Kayış},
title={Effective Training Methods for Automatic Musical Genre Classification},
booktitle={Proceedings of the 8th International Conference on Data Science, Technology and Applications - Volume 1: DATA,},
year={2019},
pages={275-280},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007933202750280},
isbn={978-989-758-377-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 8th International Conference on Data Science, Technology and Applications - Volume 1: DATA,
TI - Effective Training Methods for Automatic Musical Genre Classification
SN - 978-989-758-377-3
AU - Atsiz E.
AU - Albey E.
AU - Kayış E.
PY - 2019
SP - 275
EP - 280
DO - 10.5220/0007933202750280