Optimizing Musical Genre Classification Using Genetic Algorithms

Caio Grasso, Thiago Carvalho, Thiago Carvalho, José Franco Amaral, Pedro Coelho, Robert Oliveira, Giomar Olivera

2025

Abstract

Classifying music into genres is a challenging yet fascinating task in audio analysis. By leveraging deep learning techniques, we can automatically categorize music based on its acoustic characteristics, opening up new possibilities for organizing and understanding large music collections.The main objective of this study is to develop and evaluate deep learning models for the classification of different musical styles. To optimize the models, we utilized Genetic Algorithms (GA) to automatically determine the optimal hyperparameters and model architecture selection, including Convolutional Neural Networks and Transformers. The results demonstrated the effectiveness of GAs in exploring the hyperparameter space, leading to improved performance across multiple architectures, with EfficientNet models standing out for their consistent and robust results. This work highlights the potential of automated optimization techniques in enhancing audio analysis tasks and emphasizes the importance of integrating deep learning and evolutionary algorithms for tackling complex music classification problems.

Paper Citation

in Harvard Style

Grasso C., Carvalho T., Amaral J., Coelho P., Oliveira R. and Olivera G. (2025). Optimizing Musical Genre Classification Using Genetic Algorithms. In Proceedings of the 27th International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-758-749-8, SciTePress, pages 881-887. DOI: 10.5220/0013418200003929

in Bibtex Style

@conference{iceis25,
author={Caio Grasso and Thiago Carvalho and José Amaral and Pedro Coelho and Robert Oliveira and Giomar Olivera},
title={Optimizing Musical Genre Classification Using Genetic Algorithms},
booktitle={Proceedings of the 27th International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2025},
pages={881-887},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013418200003929},
isbn={978-989-758-749-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 27th International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - Optimizing Musical Genre Classification Using Genetic Algorithms
SN - 978-989-758-749-8
AU - Grasso C.
AU - Carvalho T.
AU - Amaral J.
AU - Coelho P.
AU - Oliveira R.
AU - Olivera G.
PY - 2025
SP - 881
EP - 887
DO - 10.5220/0013418200003929
PB - SciTePress