Music-Structure Segmentation in Balinese Gamelan (Tabuh Lelambatan) with SSM, Checkerboard Novelty, and HMM

Authors

  • Ni Nyoman Sucianta Pertiwi Institut Bisnis dan Teknologi Indonesia
  • Anak Agung Gde Bagus Ariana Informatika, Fakultas Teknologi dan Informatika, Institut Bisnis dan Teknologi Indonesia, Bali, Indonesia
  • Ni Putu Suci Meinarni Informatika, Fakultas Teknologi dan Informatika, Institut Bisnis dan Teknologi Indonesia, Bali, Indonesia
  • Ayu Gede Willdahlia Informatika, Fakultas Teknologi dan Informatika, Institut Bisnis dan Teknologi Indonesia, Bali, Indonesia
  • Made Suci Ariantini Informatika, Fakultas Teknologi dan Informatika, Institut Bisnis dan Teknologi Indonesia, Bali, Indonesia

DOI:

10.33395/sinkron.v10i1.15494

Keywords:

Balinese Gamelan, Checkerboard, Hidden Markov Model, Model, Music Information Retrieval, Self-Similarity Matrix

Abstract

This study aims to automatically segment the musical structure of Balinese gamelan by combining the Self-Similarity Matrix (SSM) method, the Checkerboard Novelty kernel, and Hidden Markov Models (HMM). Balinese gamelan has a complex musical structure that is cyclical and based on a colotomik system, requiring an adaptive analytical approach to repetitive patterns and transitions between musical sections. The research data consists of 30 Tabuh Lelambatan gamelan audio recordings obtained from public digital sources and validated through expert annotation to produce ground truth. The segmentation process was carried out through feature extraction using Constant-Q Transform (CQT), SSM formation to detect acoustic similarity patterns, application of the checkerboard kernel to mark transitions between segments, and temporal sequence modeling using HMM to refine boundary detection. System performance evaluation was carried out by comparing the segmentation results with ground truth using precision, recall, and F1-score metrics. The test results showed an average macro precision value of 0.998, a recall of 0.705, and an F1-score of 0.818, indicating that this method is capable of detecting the main boundaries of musical structures with high accuracy and consistent stability. However, the model still tends to miss gradual micro transitions. This research contributes to the field of Music Information Retrieval (MIR) and supports efforts to preserve traditional Balinese music through data-based analysis and the development of music computing technology.

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References

Altarturi, H. H. M., & Anuar, N. B. (2022). Ground Truth Dataset: Objectionable Web Content. Data, 7(11). https://doi.org/10.3390/data7110153

Ardana, I. K. (2021). Re-Actualization Balinese Gamelan Harmony1 for Renewal Knowlegde of the Balinese Music. 8(June), 51–69.

Gang, Q., Zhou, F., & Zhang, K. (2024). Hidden Markov Models ( HMMs ) for Medical Applications. 9, 1–7. https://doi.org/10.4172/aot-7.S1-1000001.Citation

Gimeno, P., Viñals, I., Ortega, A., Miguel, A., & Lleida, E. (2020). Multiclass audio segmentation based on recurrent neural networks for broadcast domain data. 1–19.

Gu, Y., Zhang, X., Xue, L., Li, H., & Wu, Z. (2024). An Investigation of Time-Frequency Representation Discriminators for High-Fidelity Vocoder. 1–11. https://doi.org/10.1109/TASLP.2024.3468005

Guan, X., Dong, Z., Liu, H., & Li, Q. (2025). Improving Phrase Segmentation in Symbolic Folk Music : A Hybrid Model with Local Context and Global Structure Awareness. 1–16.

Hasnan, H. H., & Kamarudin, K. A. D. (2024). Comparing Short-Time Fourier Transform (STFT) and Constant-Q Transform (CQT) For Spectral Analysis of The Rebana, Malay Traditional Music Instrument. 1(1), 23–45.

Kuiper, J. S. (2020). Music Structure Analysis An Exploration of and Improvements on the Distance-based Segmentation and Annotation Approach. 1–52.

Mamonto, S., Langi, Y., & Rindengan, A. (2016). Penerapan Hidden Markov Model Pada Harga Saham. D’CARTESIAN, 5(1), 35. https://doi.org/10.35799/dc.5.1.2016.12731

Mccallum, M. C. (2021). Unsupervised Learning of Deep Features for Music Segmentation.

Müller, M., & Chiu, C.-Y. (2024). A Basic Tutorial on Novelty and Activation Functions for Music Signal Processing. 7(1), 179–194.

Rodrigues, J., Liu, H., Folgado, D., Belo, D., Schultz, T., & Gamboa, H. (2022). Feature-Based Information Retrieval of Multimodal Biosignals with a Self-Similarity Matrix: Focus on Automatic Segmentation. Biosensors, 12(12), 1–34. https://doi.org/10.3390/bios12121182

Sari, A. P. (2024). Gamelan Bali Dalam Konstelasi Genetika. 15(1), 34–46.

Wang, M., Lin, Y. H., & Mikhelson, I. (2020). Regime-Switching Factor Investing with Hidden Markov Models. Journal of Risk and Financial Management, 13(12), 0–15. https://doi.org/10.3390/jrfm13120311

Yoga, I. N. W., Yudarta, I. G., & Mawan, I. G. (2024). Tabuh Pat Lelambatan Pari Anom in the Karawitan Composition and Its Psychological Influence on Gamelan Players. 3(1), 52–60. https://doi.org/10.59997/jacam.v3i1.3705

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How to Cite

Pertiwi, N. N. S. ., Ariana, A. A. G. B. ., Meinarni, N. P. S. ., Willdahlia, A. G. ., & Ariantini, M. S. . (2026). Music-Structure Segmentation in Balinese Gamelan (Tabuh Lelambatan) with SSM, Checkerboard Novelty, and HMM. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 10(1), 343-351. https://doi.org/10.33395/sinkron.v10i1.15494

Issue

Vol. 10 No. 1 (2026): Article Research January 2026

Section

Articles

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Copyright (c) 2026 Ni Nyoman Sucianta Pertiwi, Anak Agung Gde Bagus Ariana, Ni Putu Suci Meinarni, Ayu Gede Willdahlia, Made Suci Ariantini

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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