Abstract
Research on automatic music classification has gained significance in the recent years due to a significant increase in music collections size. Music is available very easily through the mobile and internet domain, so there is a need to manage music by categorizing it for search and discovery. This paper focuses on music classification by genre which is a type of supervised learning oriented problem. That means in order to build a formal classifier model it is necessary to train it using annotated data. Researchers have to build their own training sets or rely on others that are usually limited with regards to size or due to copyright restrictions. The approach described in this paper is to use the public Jamendo database for training the chosen classifier for genre recognition task.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Homburg, H., Mierswa, I., Moller, B., Morik, K., Wurst, M.: A benchmark dataset for audio classification and clustering. In: Proceedings of the International Conference on Music Information Retrieval, pp. 528–531 (2005)
McKay, C.: Automatic music classification with jMIR. Ph.D. Dissertation. McGill University, Canada (2010)
Pachet, F., Cazaly, D.: A Taxonomy of Musical Genres. In: Proc. Multimedia Information Access (RIAO), Paris, France (2000)
Aucouturier, J.-J., Pachet, F.: Representing musical genre: A state of the art. Journal of New Music Research 32(1), 83–93 (2003)
McKay, C.: jAudio: Towards a standardized extensible audio music feature extraction system. Course Paper. McGill University, Canada (2009)
McKay, C., Burgoyne, J.A., Fujinaga, I.: jMIR and ACE XML: Tools for Performing and Sharing Research in Automatic Music Classification. In: Proceedings of the Joint Conference on Digital Libraries (2009)
McKay, C., Fiebrink, R., McEnnis, D., Li, B., Fujinaga, I.: ACE: a framework for optimizing music classification. In: Proceedings of the International Conference on Music Information Retirieval (2005)
Tzanetakis, G., Essl, G., Cook, P.: Automatic Musical Genre Classification of Audio Signals. In: Proceedings of ISMIR (2001)
West, K., Cox, S.: Features and Classifier for the Automatic Classification of Musical Audio Signals. In: Proceedings of the International Conference on Music Information Retrieval (2006)
McKay, C., McEnnis, D., Fujinaga, I.: A large publicly accessible prototype audio database for music research. In: Proceedings of the ISMIR (2006)
Homburd, H., Mierswa, I., Moller, B., Morik, K., Wurst, M.: A benchmark dataset for audio classification and clustering. In: Proceedings of ISMIR (2005)
Ellis, D., Berenzweig, A., Whitman, B.: The “uspop2002” pop music data set (2003), http://labrosa.ee.columbia.edu/projects/musicsim/uspop2002.html
Silla, C.N., Koerich, A.L., Kaestner, C.A.A.: The latin music database. In: Proceedings of the ISMIR (2008)
Fujinaga, I., McEnnis, D.: On-demand Metadata Extraction Network. In: Proceedings of the Joint Conference on Digital Libraries (2006)
Cano, P., Batlle, E., Kalker, T., Haitsma, J.: A Review of Audio Fingerprinting. Journal of VLSI Signal Processing 41, 271–284 (2005)
Shah, R., Chandrayan, K., Rao, P.: Efficient Broadcast Monitoring using Audio Change Detection. In: Proceedings of the Fifth Indian International Conference on Artificial Intelligence, Tumkur, India (2011)
Shrestha, P., Kalker, T.: Audio fingerprinting in Peer-to-Peer Networks. In: Proceedings of the 5th International Symposium on Music Information Retrieval (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kleć, M. (2012). Evaluation of Jamendo Database as Training Set for Automatic Genre Recognition. In: Sombattheera, C., Loi, N.K., Wankar, R., Quan, T. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2012. Lecture Notes in Computer Science(), vol 7694. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35455-7_27
Download citation
DOI: https://doi.org/10.1007/978-3-642-35455-7_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35454-0
Online ISBN: 978-3-642-35455-7
eBook Packages: Computer ScienceComputer Science (R0)