Classification of music by mood is a growing area of research with interesting applications, including navigation of large music collections. Mood classifiers are usually based on acoustic features extracted from the music, but often they are used without knowing which ones are most effective. This paper describes how 63 acoustic features were evaluated using 2,389 music tracks to determine their individual usefulness in mood classification, before using feature selection algorithms to find the optimum combination.

Author: Baume, Chris
Affiliation: BBC Research and Development, London, UK
AES Convention: 134 (May 2013) Paper Number: 8811
Publication Date: May 4, 2013 Import into BibTeX
Subject: Education and Semantic Audio
Permalink: https://www.aes.org/e-lib/browse.cfm?elib=16712

Click to purchase paper as a non-member or login as an AES member. If your company or school subscribes to the E-Library then switch to the institutional version. If you are not an AES member and would like to subscribe to the E-Library then Join the AES!

This paper costs $33 for non-members and is free for AES members and E-Library subscribers.

Learn more about the AES E-Library

E-Library Location: (CD 134Papers)   /conv/134/8811.pdf

Start a discussion about this paper!