Abstract
Digital music collections are often organized by genre relationships or personal preferences. The target of automatic classification systems is to provide a music management limiting the listener’s effort for the labeling of a large number of songs. Many state-of-the art methods utilize low-level audio features like spectral and time domain characteristics, chroma etc. for categorization. However the impact of these features is very hard to understand; if the listener labels some music pieces as belonging to a certain category, this decision is indeed motivated by instrumentation, harmony, vocals, rhythm and further high-level descriptors from music theory. So it could be more reasonable to understand a classification model created from such intuitively interpretable features. For our study we annotated high-level characteristics (vocal alignment, tempo, key etc.) for a set of personal music categories. Then we created classification models which predict these characteristics from low-level audio features available in the AMUSE framework. The capability of this set of low level features to classify the expert descriptors is investigated in detail.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Basili, R., Serafini, A., & Stellato, A. (2004). Classification of musical genre: A machine learning approach. In Proceedings of the 5th international conference on music information retrieval Barcelona, Spain (pp. 505–508).
de Leon, P. P., & Inesta, J. (2007). Pattern recognition approach for music style identification using shallow statistical descriptors. IEEE Transactions on Systems, Man, and Cybernetics, 37(2), 248–257.
Lomax A. (1968). Folk song style and culture. Washington: American Association for the Advancement of Science.
Pachet, F., & Cazaly, D. (2000). A taxonomy of musical genres. In Proceedings content-based multimedia information access, Paris.
Pampalk, E., Flexer, A., & Widmer, G. (2005). Improvements of audio-based music similarity and genre classification. In Proceedings of 6th international conference on music information retrieval London, UK (pp. 628–633).
Temperley, D. (2007). Music and probability. Cambridge: MIT.
Vatolkin, I., Theimer, W., & Botteck, M. (2010a). Partition based feature processing for improved music classification. In Proceedings of the 34th annual conference of the German classification society, Karlsruhe.
Vatolkin, I., Theimer, W., & Botteck, M. (2010b). Amuse (Advanced MUSic Explorer) – A multitool framework for music data analysis. In Proceedings of the 11th international society for music information retrieval conference, Utrecht (pp. 33–38).
Vatolkin, I., Preuß, M., & Rudolph, G. (2011). Multi-objective feature selection in music genre and style recognition tasks. In Proceedings of the the 2011 genetic and evolutionary computation conference, Dublin (pp. 411–418).
Vembu, S., & Baumann, S. (2004). A self-organizing map based knowledge discovery for music recommendation systems. In Proceedings of the 2nd international symposium on computer music modeling and retrieval, Esbjerg.
Weihs, C., Ligges, U., Mörchen, F., & Müllensiefen, D. (2007). Classification in music research. Advances in Data Analysis and Classification, 1(3), 255–291. Springer.
Acknowledgements
We thank the Klaus Tschira Foundation for the financial support. Thanks to Uwe Ligges for statistical support.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer International Publishing Switzerland
About this paper
Cite this paper
Rötter, G., Vatolkin, I., Weihs, C. (2013). Computational Prediction of High-Level Descriptors of Music Personal Categories. In: Lausen, B., Van den Poel, D., Ultsch, A. (eds) Algorithms from and for Nature and Life. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-319-00035-0_54
Download citation
DOI: https://doi.org/10.1007/978-3-319-00035-0_54
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-00034-3
Online ISBN: 978-3-319-00035-0
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)