Music Recommendation System

Authors

DOI:

https://doi.org/10.26636/jtit.2014.2.1024

Keywords:

feature vectors, music classification, music information retrieval, music parameterization, Principal Component Analysis

Abstract

The paper focuses on optimization vector content feature for the music recommendation system. For the purpose of experiments a database is created consisting of excerpts of music files. They are assigned to 22 classes corresponding to different music genres. Various feature vectors based on low-level signal descriptors are tested and then optimized using correlation analysis and Principal Component Analysis (PCA). Results of the experiments are shown for the variety of feature vectors. Also, a music recommendation system is presented along with its main user interfaces.

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Submitted

2023-06-26

Published

2014-06-30

Issue

No. 2 (2014)

Section

ARTICLES FROM THIS ISSUE

License

Copyright (c) 2014 Journal of Telecommunications and Information Technology

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
B. Kostek, A. Kaczmarek, P. Spaleniak, and P. Hoffmann, “Music Recommendation System”, JTIT, vol. 56, no. 2, pp. 59–69, Jun. 2014, doi: 10.26636/jtit.2014.2.1024.