@inproceedings{auguin-fung-2014-co,
title = "Co-Training for Classification of Live or Studio Music Recordings",
author = "Auguin, Nicolas and
Fung, Pascale",
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Declerck, Thierry and
Loftsson, Hrafn and
Maegaard, Bente and
Mariani, Joseph and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Ninth International Conference on Language Resources and Evaluation ({LREC}'14)",
month = may,
year = "2014",
address = "Reykjavik, Iceland",
publisher = "European Language Resources Association (ELRA)",
url = "http://www.lrec-conf.org/proceedings/lrec2014/pdf/1119_Paper.pdf",
pages = "3650--3653",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Co-Training for Classification of Live or Studio Music Recordings
%A Auguin, Nicolas
%A Fung, Pascale
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Declerck, Thierry
%Y Loftsson, Hrafn
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Moreno, Asuncion
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC’14)
%D 2014
%8 May
%I European Language Resources Association (ELRA)
%C Reykjavik, Iceland
%F auguin-fung-2014-co
%X 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.
%U http://www.lrec-conf.org/proceedings/lrec2014/pdf/1119_Paper.pdf
%P 3650-3653
Markdown (Informal)
[Co-Training for Classification of Live or Studio Music Recordings](http://www.lrec-conf.org/proceedings/lrec2014/pdf/1119_Paper.pdf) (Auguin & Fung, LREC 2014)
ACL