Using Support Vector Machines for Automatic Mood Tracking in Audio Music

Panda R, Paiva RP. Using Support Vector Machines for Automatic Mood Tracking in Audio Music. In: AES Convention 130. Audio Engineering Society; 2011.pp. 8378:1-. Available from: https://aes.org/publications/elibrary-page/?id=15845

@inproceedings{Panda2011_15845,
  author = {Panda, Renato and Paiva, Rui Pedro},
  title = {{Using Support Vector Machines for Automatic Mood Tracking in Audio Music}},
  booktitle = {AES Convention 130},
  pages = {8378:1-},
  year = {2011},
  month = may,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=15845}
}

TY  - CPAPER
TI  - Using Support Vector Machines for Automatic Mood Tracking in Audio Music
AU  - Panda, Renato
AU  - Paiva, Rui Pedro
T2  - AES Convention 130
SP  - 1
M1  - Paper 8378
PY  - 2011
DA  - 2011/05/06
UR  - https://aes.org/publications/elibrary-page/?id=15845
PB  - Audio Engineering Society
LA  - en
AB  - In this paper we propose a solution for automatic mood tracking in audio music, based on supervised learning and classification. To this end, various music clips with a duration of 25 seconds, previously annotated with arousal and valence (AV) values, were used to train several models. These models were used to predict quadrants of the Thayer’s taxonomy and AV values, of small segments from full songs, revealing the mood changes over time. The system accuracy was measured by calculating the matching ratio between predicted results and full song annotations performed by volunteers. Different combinations of audio features, frameworks and other parameters were tested, resulting in an accuracy of 56.3% and showing there is still much room for improvement.
ER  -