Abstract
In recent years, a number of approaches have been developed for the automatic recognition of music genres, but also more specific categories (styles, moods, personal preferences, etc.). Among the different sources for building classification models, features extracted from the audio signal play an important role in the literature. Although such features can be extracted from any digitised music piece independently of the availability of other information sources, their extraction can require considerable computational costs and the audio alone does not always contain enough information for the identification of the distinctive properties of a musical category. In this work we consider playlists that are created and shared by music listeners as another interesting source for feature extraction and music categorisation. The main idea is that the tracks of a playlist are often from the same artist or belong to the same category, e.g. they have the same genre or style, which allows us to exploit their co-occurrences for classification tasks. In the paper, we evaluate strategies for better genre and style classification based on the analysis of larger collections of user-provided playlists and compare them to a recent classification technique from the literature. Our first results indicate that an already comparably simple playlist-based classifiers can in some cases outperform an advanced audio-based classification technique.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
More details of the training data will be given in Sect. 3.1.
- 2.
In a preliminary study, increasing this number to 20 did not lead to measurable improvements. Obviously, the optimal number depends on the category; this investigation is however beyond the scope of this first study.
- 3.
The samples included about one million playlists from Last.fm and about 600,000 playlists from 8tracks, see also Bonnin and Jannach (2014).
- 4.
The best performing method depends on the category. Moreover, the removal of a weaker classifier from ensemble of above mentioned methods led to a statistically significant reduction of performance in a previous study (Vatolkin et al. 2014).
References
Bertin-Mehieux, T., Eck, D., Maillet, F., & Lamere, P. (2008). Autotagger: A model for predicting social tags from acoustic features on large music databases. Journal of New Music Research, 37(2), 115–135.
Blume, H., Haller, M., Botteck, M., & Theimer, W. (2008). Perceptual feature based music classification - A DSP perspective for a new type of application. In Proc. IC-SAMOS (pp. 92–99).
Bonnin, G., & Jannach, D. (2014). Automated generation of music playlists: Survey and experiments. ACM Computing Surveys, 47(2), 26:1–26:35.
Celma, Ã’. (2010). Music recommendation and discovery: The long tail, long fail, and long play in the digital music space. Berlin: Springer.
Fields, B. (2011). Contextualize your Listening: The Playlist as Recommendation Engine. PhD thesis, University of London.
Han, J., & Kamber, M. (2006). Data mining: Concepts and techniques. The Morgan Kaufmann Series in Data Management Systems. San Francisco, CA: Morgan Kaufmann.
Hariri, N., Mobasher, B., & Burke, R. (2012). Context-aware music recommendation based on latent topic sequential patterns. In Proc. ACM RecSys 2013 (pp. 131–138).
Lartillot, O., & Toivainen, P. (2007). MIR in Matlab (II): A toolbox for musical feature extraction from audio. In Proc. Int’l Conf. on Music Information Retrieval (ISMIR) (pp. 127–130).
Lidy, T., Rauber, A., Pertusa, A., & Iñesta, J. M. (2007). Improving genre classification by combination of audio and symbolic descriptors using a transcription system. In Proc. Int’l Conf. on Music Information Retrieval (ISMIR) (pp. 61–66).
Mckay, C. (2010). Automatic Music Classification with jMIR. PhD thesis, McGill University.
Meng, A., Ahrendt, P., Larsen, J., & Hansen, L. K. (2007). Temporal feature integration for music genre classification. IEEE Transactions on Audio, Speech, and Language Processing, 15(5), 1654–1664.
Mierswa, I., & Morik, K. (2005). Automatic feature extraction for classifying audio data. Machine Learning Journal, 58(2–3), 127–149.
Serra, X., Magas, M., Benetos, E., Chudy, M., Dixon, S., Flexer, A., et al. (2013). Roadmap for music information research. Technical Report, The MIReS Consortium.
Smith, T., & Waterman, M. (1981). Identification of common molecular subsequences. Journal of Molecular Biology, 147, 195–197.
Sturm, B. (2014). A survey of evaluation in music genre recognition. In A. Nünberger, S. Stober, B. Larsen, & M. Detyniecki (Eds.), Adaptive multimedia retrieval: Semantics, context, and adaptation. Lecture notes in computer science (Vol. 8382, pp. 29–66). Cham:Springer.
Theimer, W., Vatolkin, I., & Eronen, A. (2008). Definitions of audio features for music content description. Technical Report TR08-2-001, TU Dortmund.
Tzanetakis, G., & Cook, P. (2002). Musical genre classification of audio signals. IEEE Transactions on Speech and Audio Processing, 10(5), 293–302.
Vatolkin, I. (2013). Improving Supervised Music Classification by Means of Multi-Objective Evolutionary Feature Selection. PhD thesis, Department of Computer Science, TU Dortmund.
Vatolkin, I., Bischl, B., Rudolph, G., & Weihs, C. (2014). Statistical comparison of classifiers for multi-objective feature selection in instrument recognition. In Data analysis, machine learning and knowledge discovery (pp. 171–178). Cham: Springer.
Weihs, C., Ligges, U., Mörchen, F., & Müllensiefen, D. (2007). Classification in music research. Advances in Data Analysis and Classification, 1(3), 255–291.
Yang, Y.-H., & Chen, H. H. (2011). Music emotion recognition. Boca Raton: CRC Press.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Vatolkin, I., Bonnin, G., Jannach, D. (2016). Comparing Audio Features and Playlist Statistics for Music Classification. In: Wilhelm, A., Kestler, H. (eds) Analysis of Large and Complex Data. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-319-25226-1_37
Download citation
DOI: https://doi.org/10.1007/978-3-319-25226-1_37
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-25224-7
Online ISBN: 978-3-319-25226-1
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)