Comparison Between Two Algorithms in Music Genre Classification

Runming Weng

2024

Abstract

Through people’s thousands of years of effort, a huge amount of music genres were created. Therefore, finding algorithms that can automatically classify the genres of music has become an essential problem in contributing modern digital music industry. Also, finding out which algorithm can complete the task more accurately can dramatically improve the efficiency in real applications like sending music that users are interested in based on the music users hear most. This study compares several algorithms in the use of music genre classification and convinces the importance of music genre classification in modern digital applications, certain the advantages and disadvantages of different algorithms. The research is mainly focused on K-Nearest Neighbors (KNN) and Convolutional Neural Networks (CNN) using the GTZAN dataset The study discusses the capability of CNN in capturing complex temporal and spectral patterns, and KNN’s effectiveness in genre identification based on feature proximity. The result proves KNN’s reliability, accuracy, and adaptability. Offering insight into the realistic usage of the algorithms in the technology-driven music industry.

Paper Citation

in Harvard Style

Weng R. (2024). Comparison Between Two Algorithms in Music Genre Classification. In Proceedings of the 1st International Conference on Data Science and Engineering - Volume 1: ICDSE; ISBN 978-989-758-690-3, SciTePress, pages 137-141. DOI: 10.5220/0012829700004547

in Bibtex Style

@conference{icdse24,
author={Runming Weng},
title={Comparison Between Two Algorithms in Music Genre Classification},
booktitle={Proceedings of the 1st International Conference on Data Science and Engineering - Volume 1: ICDSE},
year={2024},
pages={137-141},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012829700004547},
isbn={978-989-758-690-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Data Science and Engineering - Volume 1: ICDSE
TI - Comparison Between Two Algorithms in Music Genre Classification
SN - 978-989-758-690-3
AU - Weng R.
PY - 2024
SP - 137
EP - 141
DO - 10.5220/0012829700004547
PB - SciTePress