Co-Training for Classification of Live or Studio Music Recordings

Nicolas Auguin, Pascale Fung


Abstract
The fast-spreading development of online streaming services has enabled people from all over the world to listen to music. However, it is not always straightforward for a given user to find the “right” song version he or she is looking for. As streaming services may be affected by the potential dissatisfaction among their customers, the quality of songs and the presence of tags (or labels) associated with songs returned to the users are very important. Thus, the need for precise and reliable metadata becomes paramount. In this work, we are particularly interested in distinguishing between live and studio versions of songs. Specifically, we tackle the problem in the case where very little-annotated training data are available, and demonstrate how an original co-training algorithm in a semi-supervised setting can alleviate the problem of data scarcity to successfully discriminate between live and studio music recordings.
Anthology ID:
L14-1087
Volume:
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)
Month:
May
Year:
2014
Address:
Reykjavik, Iceland
Editors:
Nicoletta Calzolari, Khalid Choukri, Thierry Declerck, Hrafn Loftsson, Bente Maegaard, Joseph Mariani, Asuncion Moreno, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
3650–3653
Language:
URL:
http://www.lrec-conf.org/proceedings/lrec2014/pdf/1119_Paper.pdf
DOI:
Bibkey:
Cite (ACL):
Nicolas Auguin and Pascale Fung. 2014. Co-Training for Classification of Live or Studio Music Recordings. In Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14), pages 3650–3653, Reykjavik, Iceland. European Language Resources Association (ELRA).
Cite (Informal):
Co-Training for Classification of Live or Studio Music Recordings (Auguin & Fung, LREC 2014)
Copy Citation:
PDF:
http://www.lrec-conf.org/proceedings/lrec2014/pdf/1119_Paper.pdf