Evaluation of Acoustic Features for Music Emotion Recognition
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C. Baume, "Evaluation of Acoustic Features for Music Emotion Recognition," Paper 8811, (2013 May.). doi:
C. Baume, "Evaluation of Acoustic Features for Music Emotion Recognition," Paper 8811, (2013 May.). doi:
Abstract: 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.
@article{baume2013evaluation,
author={baume, chris},
journal={journal of the audio engineering society},
title={evaluation of acoustic features for music emotion recognition},
year={2013},
volume={},
number={},
pages={},
doi={},
month={may},}
@article{baume2013evaluation,
author={baume, chris},
journal={journal of the audio engineering society},
title={evaluation of acoustic features for music emotion recognition},
year={2013},
volume={},
number={},
pages={},
doi={},
month={may},
abstract={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.},}
TY - paper
TI - Evaluation of Acoustic Features for Music Emotion Recognition
SP -
EP -
AU - Baume, Chris
PY - 2013
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - May 2013
TY - paper
TI - Evaluation of Acoustic Features for Music Emotion Recognition
SP -
EP -
AU - Baume, Chris
PY - 2013
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - May 2013
AB - 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.
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, 2013Import into BibTeX
Subject:
Education and Semantic Audio
Permalink:
http://www.aes.org/e-lib/browse.cfm?elib=16712
