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Complexity–entropy causality plane: A useful approach for distinguishing songs

Author

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  • Ribeiro, Haroldo V.
  • Zunino, Luciano
  • Mendes, Renio S.
  • Lenzi, Ervin K.

Abstract

Nowadays we are often faced with huge databases resulting from the rapid growth of data storage technologies. This is particularly true when dealing with music databases. In this context, it is essential to have techniques and tools able to discriminate properties from these massive sets. In this work, we report on a statistical analysis of more than ten thousand songs aiming to obtain a complexity hierarchy. Our approach is based on the estimation of the permutation entropy combined with an intensive complexity measure, building up the complexity–entropy causality plane. The results obtained indicate that this representation space is very promising to discriminate songs as well as to allow a relative quantitative comparison among songs. Additionally, we believe that the here-reported method may be applied in practical situations since it is simple, robust and has a fast numerical implementation.

Suggested Citation

  • Ribeiro, Haroldo V. & Zunino, Luciano & Mendes, Renio S. & Lenzi, Ervin K., 2012. "Complexity–entropy causality plane: A useful approach for distinguishing songs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(7), pages 2421-2428.
    • Handle: RePEc:eee:phsmap:v:391:y:2012:i:7:p:2421-2428
    DOI: 10.1016/j.physa.2011.12.009
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    Citations

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    Cited by:

    1. Zhang, Boyi & Shang, Pengjian & Zhou, Qin, 2021. "The identification of fractional order systems by multiscale multivariate analysis," Chaos, Solitons & Fractals, Elsevier, vol. 144(C).
    2. Fernandes, Leonardo H.S. & Araújo, Fernando H.A., 2020. "Taxonomy of commodities assets via complexity-entropy causality plane," Chaos, Solitons & Fractals, Elsevier, vol. 137(C).
    3. Fernandes, Leonardo H.S. & de Araújo, Fernando H.A. & Silva, Igor E.M. & Neto, Jusie S.P., 2021. "Macroeconophysics indicator of economic efficiency," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 573(C).
    4. Leite, Gustavo de Novaes Pires & Araújo, Alex Maurício & Rosas, Pedro André Carvalho & Stosic, Tatijana & Stosic, Borko, 2019. "Entropy measures for early detection of bearing faults," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 458-472.
    5. Dai, Yimei & He, Jiayi & Wu, Yue & Chen, Shijian & Shang, Pengjian, 2019. "Generalized entropy plane based on permutation entropy and distribution entropy analysis for complex time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 520(C), pages 217-231.
    6. Dai, Yimei & Zhang, Hesheng & Mao, Xuegeng & Shang, Pengjian, 2018. "Complexity–entropy causality plane based on power spectral entropy for complex time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 501-514.
    7. Fernandes, Leonardo H.S. & de Araujo, Fernando H.A. & Tabak, Benjamin M., 2021. "Insights from the (in)efficiency of Chinese sectoral indices during COVID-19," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 578(C).
    8. Stosic, Darko & Stosic, Dusan & Ludermir, Teresa B. & Stosic, Tatijana, 2019. "Exploring disorder and complexity in the cryptocurrency space," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 548-556.
    9. Zunino, Luciano & Ribeiro, Haroldo V., 2016. "Discriminating image textures with the multiscale two-dimensional complexity-entropy causality plane," Chaos, Solitons & Fractals, Elsevier, vol. 91(C), pages 679-688.
    10. de Novaes Pires Leite, Gustavo & da Cunha, Guilherme Tenório Maciel & dos Santos Junior, José Guilhermino & Araújo, Alex Maurício & Rosas, Pedro André Carvalho & Stosic, Tatijana & Stosic, Borko & Ros, 2021. "Alternative fault detection and diagnostic using information theory quantifiers based on vibration time-waveforms from condition monitoring systems: Application to operational wind turbines," Renewable Energy, Elsevier, vol. 164(C), pages 1183-1194.
    11. Martins, Adriel M.F. & Fernandes, Leonardo H.S. & Nascimento, Abraão D.C., 2023. "Scientific progress in information theory quantifiers," Chaos, Solitons & Fractals, Elsevier, vol. 170(C).
    12. Jauregui, M. & Zunino, L. & Lenzi, E.K. & Mendes, R.S. & Ribeiro, H.V., 2018. "Characterization of time series via Rényi complexity–entropy curves," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 498(C), pages 74-85.
    13. Gündüz, Güngör, 2023. "Entropy, energy, and instability in music," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
    14. Yin, Yi & Shang, Pengjian, 2016. "Weighted permutation entropy based on different symbolic approaches for financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 443(C), pages 137-148.
    15. Ferreira, Paulo & Quintino, Derick & Wundervald, Bruna & Dionísio, Andreia & Aslam, Faheem & Cantarinha, Ana, 2021. "Is Brazilian music getting more predictable? A statistical physics approach for different music genres," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 583(C).

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