Evaluation of Acoustic Features for Music Emotion Recognition

Baume C. Evaluation of Acoustic Features for Music Emotion Recognition. In: AES Convention 134. Audio Engineering Society; 2013. Paper 8811. Available from: https://aes.org/publications/elibrary-page/?id=16712

@inproceedings{Baume2013_16712,
  author = {Baume, Chris},
  title = {{Evaluation of Acoustic Features for Music Emotion Recognition}},
  booktitle = {AES Convention 134},
  note = {Paper 8811},
  year = {2013},
  month = may,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=16712}
}

TY  - CPAPER
TI  - Evaluation of Acoustic Features for Music Emotion Recognition
AU  - Baume, Chris
T2  - AES Convention 134
M1  - Paper 8811
PY  - 2013
DA  - 2013/05/06
UR  - https://aes.org/publications/elibrary-page/?id=16712
PB  - Audio Engineering Society
LA  - en
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.
ER  -