loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Authors: Caio Grasso 1 ; Thiago Carvalho 1 ; 2 ; José Franco Amaral 1 ; Pedro Coelho 1 ; Robert Oliveira 1 and Giomar Olivera 1

Affiliations: 1 FEN/UERJ, Rio de Janeiro State University, Rio de Janeiro, Brazil ; 2 Electrical Engineering Department, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, Brazil

Keyword(s): Genetic Algorithm, Music Classification, Signal Processing, Deep Learning.

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 o f integrating deep learning and evolutionary algorithms for tackling complex music classification problems. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 13.59.234.246

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Grasso, C., Carvalho, T., Amaral, J. F., 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; ISSN 2184-4992, SciTePress, pages 881-887. DOI: 10.5220/0013418200003929

@conference{iceis25,
author={Caio Grasso and Thiago Carvalho and José Franco 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},
issn={2184-4992},
}

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
IS - 2184-4992
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

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic