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On the complex network structure of musical pieces: analysis of some use cases from different music genres

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Abstract

This paper focuses on the modeling of musical melodies as networks. Notes of a melody can be treated as nodes of a network. Connections are created whenever notes are played in sequence. We analyze some main tracks coming from different music genres, with melodies played using different musical instruments. We find out that the considered networks are, in general, scale free networks and exhibit the small world property. We measure the main metrics and assess whether these networks can be considered as formed by sub-communities. Outcomes confirm that peculiar features of the tracks can be extracted from this analysis methodology. This approach can have an impact in several multimedia applications such as music didactics, multimedia entertainment, and digital music generation.

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Notes

  1. A bar (often referred as measure) is a segment of time corresponding to a specific number of beats in which notes are played. Dividing music into bars provides regular reference points to pinpoint locations within a piece of music.

  2. In music, the “tonic” is the first scale degree of a diatonic scale. It is thus the tonal center of a given key; in other words, it is the main note of that key.

  3. In music, the “dominant” note in a given key is the fifth scale degree of the diatonic scale: It is called dominant because it is next in importance to the tonic.

References

  1. Angeler DG (2016) Heavy metal music meets complexity and sustainability science. SpringerPlus 5(1):1637

    Article  Google Scholar 

  2. Bastian M, Heymann S, Jacomy M (2009) Gephi: an open source software for exploring and manipulating networks

  3. Bell C (2011) Algorithmic music composition using dynamic Markov chains and genetic algorithms. J Comput Sci Coll 27(2):99–107

    MathSciNet  Google Scholar 

  4. Berenzweig A, Logan B, Ellis DPW, Whitman BPW (2004) A large-scale evaluation of acoustic and subjective music-similarity measures. Comput Music J 28 (2):63–76

    Article  Google Scholar 

  5. Biemann C, Roos S, Weihe K (2012) Quantifying semantics using complex network analysis. In: Proceedings of the 24th International Conference on Computational Linguistics, COLING 2012, Technical papers, 8-15 December 2012, Mumbai, India, pp 263–278

  6. Blondel VD, Guillaume J-L, Lambiotte R, Lefebvre E (2008) Fast unfolding of communities in large networks. J Stat Mech: Theory Exp 2008(10):P10008

    Article  Google Scholar 

  7. Boccaletti S, Latora V, Moreno Y, Chavez M, Hwang D-U (2006) Complex networks: structure and dynamics. Phys Rep 424(4–5):175–308

    Article  MathSciNet  MATH  Google Scholar 

  8. Cancho RFi, Solé RV (2001) The small world of human language. Proc R Soc Lond B Biol Sci 268(1482):2261–2265

    Article  Google Scholar 

  9. Cong J, Liu H (2014) Approaching human language with complex networks. Phys Life Rev 11(4):598–618

    Article  Google Scholar 

  10. Cootes AP, Muggleton SH, Sternberg MJ (2007) The identification of similarities between biological networks: application to the metabolome and interactome. J Mol Biol 369(4):1126–1139

    Article  Google Scholar 

  11. Fell DA, Wagner A (2000) The small world of metabolism. Nat Biotech 18 (11):1121–1122

    Article  Google Scholar 

  12. Ferretti S (2013) Gossiping for resource discovering: an analysis based on complex network theory. Futur Gener Comput Syst 29(6):1631–1644

    Article  Google Scholar 

  13. Ferretti S (2013) Shaping opportunistic networks. Comput Commun 36(5):481–503

    Article  Google Scholar 

  14. Ferretti S (2016) Guitar solos as networks. In: 2016 IEEE International Conference on Multimedia and Expo (ICME), pp 1–6

  15. Ferretti S (2017) On the modeling of musical solos as complex networks. Inf Sci 375:271–295

    Article  Google Scholar 

  16. Fu Z, Lu G, Ting KM, Zhang D (2011) A survey of audio-based music classification and annotation. Trans Multi 13(2):303–319

    Article  Google Scholar 

  17. Grabska-Gradzińska I, Kulig A, Kwapien J, Drozdz S (2012) Complex network analysis of literary and scientific texts. Int J Mod Phys C 23(07):1–15

    Google Scholar 

  18. Granroth-Wilding M, Steedman M (2014) A robust parser-interpreter for jazz chord sequences. J New Music Res 0(0):1–20

    Google Scholar 

  19. Humphries MD, Gurney K (2008) Network small-world-ness: a quantitative method for determining canonical network equivalence. PLOS ONE 3(4):1–10. 04

    Article  Google Scholar 

  20. Keller R, Schofield A, Toman-Yih A, Merritt Z, Elliott J (2013) Automating the explanation of jazz chord progressions using idiomatic analysis. Comput Music J 37(4):54–69

    Article  Google Scholar 

  21. Knopke I, Jürgensen F (2011) Chapter symbolic data mining in musicology. Chapman & hall/CRC data mining and knowledge discovery series. CRC Press, Boca Raton, pp 327–345. 0

    Google Scholar 

  22. Lichtenwalter R, Lichtenwalter K, Chawla NV (2010) A machine-learning approach to autonomous music composition. J Intell Syst 19(2):95–124

    Google Scholar 

  23. Liu S, Zhang Z, Qi L, Ma M (2016) A fractal image encoding method based on statistical loss used in agricultural image compression. Multimed Tool Appl 75(23):15525–15536

    Article  Google Scholar 

  24. Liu S, Fu W, He L, Zhou J, Ma M (2017) Distribution of primary additional errors in fractal encoding method. Multimed Tool Appl 76(4):5787–5802

    Article  Google Scholar 

  25. Liu XF, Tse CK, Small M (2010) Complex network structure of musical compositions algorithmic generation of appealing music. Physica A 389(1):126–132

    Article  Google Scholar 

  26. Liu Y, Nie L, Liu L, Rosenblum DS (2016) From action to activity: sensor-based activity recognition. Neurocomputing 181:108–115. Big Data Driven Intelligent Transportation Systems

    Article  Google Scholar 

  27. Manaris B, Romero J, Machado P, Krehbiel D, Hirzel T, Pharr W, Davis RB (2005) Zipf’s law, music classification, and aesthetics. Comput Music J 29(1):55–69

    Article  Google Scholar 

  28. Math - Commons-Math: The Apache Commons Mathematics Library

  29. Montoya JM, Solé RV (2002) Small world patterns in food webs. J Theor Biol 214(3):405–412

    Article  Google Scholar 

  30. Musicxml web site, http://www.musicxml.com/

  31. Newman M (2010) Networks: an introduction. Oxford University Press, Inc., New York

    Book  MATH  Google Scholar 

  32. O’Madadhain J, Fisher D, White S, Boey Y (2003) The JUNG (java universal Network/Graph) framework. Technical report, UCI-ICS

  33. Oord Avd, Dieleman S, Schrauwen B (2013) Deep content-based music recommendation. In: Proceedings of the 26th International Conference on Neural Information Processing Systems, NIPS’13. Curran Associates Inc, USA, pp 2643–2651

  34. Pardo T, Antiqueira L, das Gracas Nunes M, Oliveira ON, da Fontoura Costa L (2006) Using complex networks for language processing: the case of summary evaluation. In: Proceedings of the 2006 International Conference on Communications, Circuits and Systems, vol 4, pp 2678–2682

  35. Patra BG, Das D, Bandyopadhyay S (2013) Unsupervised approach to hindi music mood classification. In: Proceedings of the First International Conference on Mining Intelligence and Knowledge Exploration - vol 8284, MIKE 2013. Springer, New York, pp 62–69

  36. Pyguitarpro web site

  37. Shah N, Koutra D, Zou T, Gallagher B, Faloutsos C (2015) TimeCrunch: interpretable dynamic graph summarization. In: Proceedings of the 21st ACM international conference on knowledge discovery and data mining (SIGKDD)

  38. Thickstun J, Harchaoui Z, Kakade S (2016) Learning features of music from scratch. arXiv:http://arXiv.org/abs/1611.09827

  39. Watts DJ, Strogatz SH (1998) Collective dynamics of’small-world’networks. Nature 393(6684):409–10

    Article  Google Scholar 

  40. Yan C, Zhang Y, Xu J, Dai F, Li L, Dai Q, Wu F (2014) A highly parallel framework for hevc coding unit partitioning tree decision on many-core processors. IEEE Signal Process Lett 21(5):573–576

    Article  Google Scholar 

  41. Yan C, Zhang Y, Xu J, Dai F, Zhang J, Dai Q, Wu F (2014) Efficient parallel framework for hevc motion estimation on many-core processors. IEEE Trans Circuits Syst Video Technol 24(12):2077–2089

    Article  Google Scholar 

  42. Zhao S, Yao H, Wang F, Jiang X, Zhang W (2014) Emotion based image musicalization. In: 2014 IEEE International conference on multimedia and expo workshops (ICMEW), pp 1–6

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Authors and Affiliations

  1. Department of Computer Science and Engineering, University of Bologna, Mura Anteo Zamboni 7, Bologna, Italy

    Stefano Ferretti

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  1. Stefano Ferretti
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Correspondence to Stefano Ferretti.

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Ferretti, S. On the complex network structure of musical pieces: analysis of some use cases from different music genres. Multimed Tools Appl 77, 16003–16029 (2018). https://doi.org/10.1007/s11042-017-5175-y

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  • DOI: https://doi.org/10.1007/s11042-017-5175-y

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