Abstract
The feature extraction is a very important step in the music audio classification. This task has been performed by renowned descriptors using, in most cases, the time-frequency approach. In this article we propose a descriptor that performs the feature extraction in a set of music audio files labeled in symphonic and percussive music, using parameters calculated within the Euclidean domain. First we calculate the variance fluctuation series of music signal, after we map this series into visibility graphs [13]. At the end each audio track will correspond to a network, where the links are defined by the visibility of variance fluctuations of their respective audio signal. Then, we measure the strength of the partitions of each network in clusters, using calculation of modularity. The results of computation of this parameter in sixty networks showed that percussive and symphonic music can be distinguished and hierarchized on a growing rang, following a direct correlation with modularity.
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
Andjelkovi´c, M., Gupte, N., Tadi´c, B.: Hidden geometry of traffic jamming. Physical Review E 91(5), 052,817 (2015)
Bergstra, J., Casagrande, N., Eck, D.: Two algorithms for timbre and rhythm-based multiresolution audio classification. In: Proceedings of ISMIR (2005)
Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. Journal of statistical mechanics: theory and experiment 2008(10), P10,008 (2008)
Buld´u, J.M., Cano, P., Koppenberger, M., Almendral, J.A., Boccaletti, S.: The complex network of musical tastes. New Journal of Physics 9(6), 172 (2007)
Correa, D.C., Saito, J.H., da F Costa, L.: Musical genres: beating to the rhythms of different drums. New Journal of Physics 12(5), 053,030 (2010)
Costa, Y.M., Oliveira, L., Koerich, A.L., Gouyon, F., Martins, J.: Music genre classification using lbp textural features. Signal Processing 92(11), 2723–2737 (2012)
Eronen, A.: Signal processing methods for audio classification and music content analysis. Tampereen teknillinen yliopisto. Julkaisu-Tampere University of Technology. Publication; 817 (2009)
Ezzaidi, H., Rouat, J.: Automatic musical genre classification using divergence and average information measures. World Academy of Science, Engineering and Technology 15 (2006)
Goulart, A.J.H.: Classificac¸ ˜ao autom´atica de gˆenero musical baseada em entropia e fractais. Ph.D. thesis, Universidade de S˜ao Paulo
Guaus, E., et al.: Audio content processing for automatic music genre classification: descriptors, databases, and classifiers (2009)
Jacobson, K., Sandler, M.B., Fields, B.: Using audio analysis and network structure to identify communities in on-line social networks of artists. In: ISMIR, pp. 269–274 (2008)
Jennings, H.D., Ivanov, P.C., Martins, A.d.M., da Silva, P., Viswanathan, G.: Variance fluctuations in nonstationary time series: a comparative study of music genres. Physica A: Statistical Mechanics and its Applications 336(3), 585–594 (2004)
Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008)
Lacasa, L., Luque, B., Luque, J., Nuno, J.C.: The visibility graph: A new method for estimating the hurst exponent of fractional brownian motion. EPL (Europhysics Letters) 86(3), 30,001 (2009)
Lacasa, L., Toral, R.: Description of stochastic and chaotic series using visibility graphs. Physical Review E 82(3), 036,120 (2010)
Melo, D.F.P.: An´alise de flutuac¸ ˜oes de variˆancia em sinais de ´audio agrupados por gˆenero musical. Proceeding Series of the Brazilian Society of Computational and Applied Mathematics 1(1) (2013)
Newman, M.E.: Analysis of weighted networks. Physical review E 70(5), 056,131 (2004)
Nunez, A., Lacasa, L., Valero, E., G´omez, J.P., Luque, B.: Detecting series periodicity with horizontal visibility graphs. International Journal of Bifurcation and Chaos 22(07), 1250,160 (2012)
Pampalk, E., Rauber, A., Merkl, D.: Content-based organization and visualization of music archives. In: Proceedings of the tenth ACM international conference on Multimedia, pp. 570–579. ACM (2002)
Panagakis, Y., Kotropoulos, C., Arce, G.R.: Music genre classification via sparse representations of auditory temporal modulations. In: Signal Processing Conference, 2009 17th European, pp. 1–5. IEEE (2009)
Park, D., Bae, A., Schich, M., Park, J.: Topology and evolution of the network of western classical music composers. EPJ Data Science 4(1), 1 (2015)
Schedl, M., G´omez, E., Urbano, J., et al.: Music information retrieval: Recent developments and applications. Now Publ. (2014)
Silla Jr, C.N., Kaestner, C.A., Koerich, A.L.: Automatic genre classification of latin music using ensemble of classifiers. In: Proc. of the 33rd Intigrated Software and Hardware Seminar, pp. 47–53 (2006)
Stephen, M., Gu, C., Yang, H.: Visibility graph based time series analysis. PloS one 10(11), e0143,015 (2015)
Tse, C., Liu, X., Small, M.: Analyzing and composing music with complex networks: finding structures in bach, chopin and mozart (2008)
Tzanetakis, G., Cook, P.: Musical genre classification of audio signals. IEEE Transactions on speech and audio processing 10(5), 293–302 (2002)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
de Freitas Piedade Melo, D., de Sousa Fadigas, I., de Barros Pereira, H.B. (2017). Community detection in visibility networks: an approach to categorize percussive influence on audio musical signals. In: Cherifi, H., Gaito, S., Quattrociocchi, W., Sala, A. (eds) Complex Networks & Their Applications V. COMPLEX NETWORKS 2016 2016. Studies in Computational Intelligence, vol 693. Springer, Cham. https://doi.org/10.1007/978-3-319-50901-3_26
Download citation
DOI: https://doi.org/10.1007/978-3-319-50901-3_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-50900-6
Online ISBN: 978-3-319-50901-3
eBook Packages: EngineeringEngineering (R0)