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

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.