%0 Conference Paper %1 Sordo:ISMIR2008 %A Sordo, M. %A Celma, O. %A Blech, M. %A Guaus, E. %C Philadelphia, USA %D 2008 %J Proceedings of the 9th International Conference on Music Information Retrieval %K LSA, Last.fm, PhD, agreement, classification, community, components, cosine, crowds distance, experiments, experts, folksonomy, genre, mp3.com, of similarity, tags, taxonomy, wisdom %T The Quest for Musical Genres: Do the Experts and the Wisdom of Crowds Agree? %X This paper presents some findings around musical genres. The main goal is to analyse whether there is any agreement between a group of experts and a community, when defining a set of genres and their relationships. For this purpose, three different experiments are conducted using two datasets: the MP3.com expert taxonomy, and last.fm tags at artist level. The experimental results show a clear agreement for some components of the taxonomy (Blues, Hip-Hop), whilst in other cases (e.g. Rock) there is no correlations. Interestingly enough, the same results are found in the MIREX2007 results for audio genre classification task. Therefore, a multi–faceted approach for musical genre using expert based classifications, dynamic associations derived from the wisdom of crowds, and content–based analysis can improve genre classification, as well as other relevant MIR tasks such as music similarity or music recommendation.

@inproceedings{Sordo:ISMIR2008, abstract = {This paper presents some findings around musical genres. The main goal is to analyse whether there is any agreement between a group of experts and a community, when defining a set of genres and their relationships. For this purpose, three different experiments are conducted using two datasets: the MP3.com expert taxonomy, and last.fm tags at artist level. The experimental results show a clear agreement for some components of the taxonomy (Blues, Hip-Hop), whilst in other cases (e.g. Rock) there is no correlations. Interestingly enough, the same results are found in the MIREX2007 results for audio genre classification task. Therefore, a multi–faceted approach for musical genre using expert based classifications, dynamic associations derived from the wisdom of crowds, and content–based analysis can improve genre classification, as well as other relevant MIR tasks such as music similarity or music recommendation.}, added-at = {2009-01-08T17:06:55.000+0100}, address = {Philadelphia, USA}, author = {Sordo, M. and Celma, O. and Blech, M. and Guaus, E.}, biburl = {https://www.bibsonomy.org/bibtex/2146e68c8b0e0fdd370bc0a2a35d347e3/ocelma}, description = {PhD}, interhash = {e55f7fbf7a89392535c438a5c6009747}, intrahash = {146e68c8b0e0fdd370bc0a2a35d347e3}, journal = {Proceedings of the 9th International Conference on Music Information Retrieval}, keywords = {LSA, Last.fm, PhD, agreement, classification, community, components, cosine, crowds distance, experiments, experts, folksonomy, genre, mp3.com, of similarity, tags, taxonomy, wisdom}, timestamp = {2009-01-08T17:21:21.000+0100}, title = {The Quest for Musical Genres: Do the Experts and the Wisdom of Crowds Agree?}, year = 2008 }