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Decoding rhythmic complexity: A nonlinear dynamics approach via visibility graphs for classifying asymmetrical rhythmic frameworks of Turkish classical music

Author

Listed:
  • Mirza, Fuat Kaan
  • Baykaş, Tunçer
  • Hekimoğlu, Mustafa
  • Pekcan, Önder
  • Tunçay, Gönül Paçacı

Abstract

The non-isochronous, hierarchical rhythmic cycles (usuls) of Turkish Classical Music (TCM) exhibit emergent temporal structures that challenge conventional rhythm analysis based on metrical regularity. To address this challenge, this study presents a complexity-oriented framework for usul classification, grounded in nonlinear time series analysis and network-based representations. Rhythmic signals are processed through energy envelope extraction, diffusion entropy analysis, and spectral transformations to capture multiscale temporal dynamics. Visibility graphs (VGs) are constructed from these representations to encode underlying structural complexity and temporal dependencies. Features derived from VG adjacency matrices serve as complexity-sensitive descriptors and enable high-accuracy classification (0.99) across 40 usul classes and 628 compositions. Energy envelope-derived graphs provide the most discriminative information, highlighting the importance of amplitude modulation in encoding rhythmic structure. Beyond classification, the analysis reveals self-organizing patterns and signatures of complexity, such as quasi-periodicity, scale-dependent variability, and entropy saturation, suggesting that usuls function as adaptive, nonlinear systems rather than metrically constrained patterns. The topological features extracted from the resulting graphs align with theoretical constructs from complexity science, such as modularity and long-range temporal correlations. This positions usul as an exemplary case for studying structured temporal complexity in cultural artifacts through the lens of dynamical systems. These findings contribute to computational rhythm analysis by demonstrating the efficacy of complexity measures in characterizing culturally specific rhythmic systems.

Suggested Citation

  • Mirza, Fuat Kaan & Baykaş, Tunçer & Hekimoğlu, Mustafa & Pekcan, Önder & Tunçay, Gönül Paçacı, 2025. "Decoding rhythmic complexity: A nonlinear dynamics approach via visibility graphs for classifying asymmetrical rhythmic frameworks of Turkish classical music," Applied Mathematics and Computation, Elsevier, vol. 507(C).
  • Handle: RePEc:eee:apmaco:v:507:y:2025:i:c:s0096300325002917
    DOI: 10.1016/j.amc.2025.129565
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    References listed on IDEAS

    as
    1. Artun, E. Can & Keçoğlu, Ibrahim & Türkoğlu, Alpar & Berker, A. Nihat, 2023. "Multifractal spin-glass chaos projection and interrelation of multicultural music and brain signals," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).
    2. Zandi-Mehran, Nazanin & Nazarimehr, Fahimeh & Rajagopal, Karthikeyan & Ghosh, Dibakar & Jafari, Sajad & Chen, Guanrong, 2022. "FFT bifurcation: A tool for spectrum analyzing of dynamical systems," Applied Mathematics and Computation, Elsevier, vol. 422(C).
    3. Hu, Jun & Zhang, Yujie & Wu, Peng & Li, Huijia, 2022. "An analysis of the global fuel-trading market based on the visibility graph approach," Chaos, Solitons & Fractals, Elsevier, vol. 154(C).
    4. Teng, Long, 2022. "Gradient boosting-based numerical methods for high-dimensional backward stochastic differential equations," Applied Mathematics and Computation, Elsevier, vol. 426(C).
    5. Yin, Yi & Shang, Pengjian, 2016. "Forecasting traffic time series with multivariate predicting method," Applied Mathematics and Computation, Elsevier, vol. 291(C), pages 266-278.
    6. Li, Sange & Shang, Pengjian, 2021. "Analysis of nonlinear time series using discrete generalized past entropy based on amplitude difference distribution of horizontal visibility graph," Chaos, Solitons & Fractals, Elsevier, vol. 144(C).
    7. Hu, Yuntong & Xiao, Fuyuan, 2022. "An efficient forecasting method for time series based on visibility graph and multi-subgraph similarity," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    8. Liu, Jin-Long & Yu, Zu-Guo & Zhou, Yu, 2024. "A cross horizontal visibility graph algorithm to explore associations between two time series," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).
    9. Li, Kun & Xu, Haocheng & Liu, Xiao, 2022. "Analysis and visualization of accidents severity based on LightGBM-TPE," Chaos, Solitons & Fractals, Elsevier, vol. 157(C).
    10. Archer, Kellie J. & Kimes, Ryan V., 2008. "Empirical characterization of random forest variable importance measures," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 2249-2260, January.
    11. Li, Kun & Zhong, PeiYun & Dong, Li & Wang, LingMin & Jiang, Luo-Luo, 2025. "OP-HHO based feature selection improves the performance of depression classification framework: A gender biased multiband research," Applied Mathematics and Computation, Elsevier, vol. 495(C).
    12. Xu, Paiheng & Zhang, Rong & Deng, Yong, 2018. "A novel visibility graph transformation of time series into weighted networks," Chaos, Solitons & Fractals, Elsevier, vol. 117(C), pages 201-208.
    13. Hosseinpour, Mahsa & Ghaemi, Sehraneh & Khanmohammadi, Sohrab & Daneshvar, Sabalan, 2022. "A hybrid high‐order type‐2 FCM improved random forest classification method for breast cancer risk assessment," Applied Mathematics and Computation, Elsevier, vol. 424(C).
    14. Mirza, Fuat Kaan & Baykaş, Tunçer & Hekimoğlu, Mustafa & Pekcan, Önder & Tunçay, Gönül Paçacı, 2024. "Decoding compositional complexity: Identifying composers using a model fusion-based approach with nonlinear signal processing and chaotic dynamics," Chaos, Solitons & Fractals, Elsevier, vol. 187(C).
    15. Nasrolahzadeh, Mahda & Mohammadpoory, Zeynab & Haddadnia, Javad, 2023. "Indices from visibility graph complexity of spontaneous speech signal: An efficient nonlinear tool for Alzheimer's disease diagnosis," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).
    16. Qiao, Honghai & Deng, Zhenghong & Li, Huijia & Hu, Jun & Song, Qun & Xia, Chengyi, 2021. "Complex networks from time series data allow an efficient historical stage division of urban air quality information," Applied Mathematics and Computation, Elsevier, vol. 410(C).
    17. Cai, Shi-Min & Zhou, Pei-Ling & Yang, Hui-Jie & Yang, Chun-Xia & Wang, Bing-Hong & Zhou, Tao, 2006. "Diffusion entropy analysis on the scaling behavior of financial markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 367(C), pages 337-344.
    18. Lv, Ya-jun & Wang, Jun-wei & Wang, Julian & Xiong, Cheng & Zou, Liang & Li, Ly & Li, Da-wang, 2020. "Steel corrosion prediction based on support vector machines," Chaos, Solitons & Fractals, Elsevier, vol. 136(C).
    19. Bai, Shiwei & Niu, Min, 2022. "The visibility graph of n-bonacci sequence," Chaos, Solitons & Fractals, Elsevier, vol. 163(C).
    20. Richard P. Taylor & Adam P. Micolich & David Jonas, 1999. "Fractal analysis of Pollock's drip paintings," Nature, Nature, vol. 399(6735), pages 422-422, June.
    21. Lahmiri, Salim & Tadj, Chakib & Gargour, Christian & Bekiros, Stelios, 2023. "Optimal tuning of support vector machines and k-NN algorithm by using Bayesian optimization for newborn cry signal diagnosis based on audio signal processing features," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).
    22. Lin, Guancen & Lin, Aijing, 2022. "Modified multiscale sample entropy and cross-sample entropy based on horizontal visibility graph," Chaos, Solitons & Fractals, Elsevier, vol. 165(P1).
    23. Bajoulvand, Atena & Zargari Marandi, Ramtin & Daliri, Mohammad Reza & Sabzpoushan, Seyed Hojjat, 2017. "Analysis of folk music preference of people from different ethnic groups using kernel-based methods on EEG signals," Applied Mathematics and Computation, Elsevier, vol. 307(C), pages 62-70.
    24. Galasso, Joseph & Cao, Duy M. & Hochberg, Robert, 2022. "A random forest model for forecasting regional COVID-19 cases utilizing reproduction number estimates and demographic data," Chaos, Solitons & Fractals, Elsevier, vol. 156(C).
    25. Telesca, Luciano & Lovallo, Michele & Pierini, Jorge O., 2012. "Visibility graph approach to the analysis of ocean tidal records," Chaos, Solitons & Fractals, Elsevier, vol. 45(9), pages 1086-1091.
    26. Yu, Guihai & Li, Xingfu & He, Deyan, 2022. "Topological indices based on 2- or 3-eccentricity to predict anti-HIV activity," Applied Mathematics and Computation, Elsevier, vol. 416(C).
    27. Ren, Weikai & Jin, Zhijun, 2023. "Phase space visibility graph," Chaos, Solitons & Fractals, Elsevier, vol. 176(C).
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