Classification of Gamelan Selonding Music Using Convolutional Neural Network

Ni Putu Diah Pradnya Savitri; Anak Agung Gde Bagus  Ariana; Ni Kadek Nita Noviani  Pande; I Made Dwi Putra  Asana; I Gusti Agung  Indrawan

doi:10.56705/ijodas.v6i3.358

Authors

Ni Putu Diah Pradnya Savitri Institut Bisnis dan Teknologi Indonesia
Anak Agung Gde Bagus Ariana Institut Bisnis dan Teknologi Indonesia
Ni Kadek Nita Noviani Pande Institut Bisnis dan Teknologi Indonesia
I Made Dwi Putra Asana Institut Bisnis dan Teknologi Indonesia
I Gusti Agung Indrawan Institut Bisnis dan Teknologi Indonesia

DOI:

https://doi.org/10.56705/ijodas.v6i3.358

Keywords:

Gamelan Selonding, CNN, MFCC, CQT, Audio Classification, Cultural Preservation

Abstract

Introduction: Balinese Selonding gamelan is an endangered sacred repertoire, and automatic recognition of its musical pieces can support documentation and preservation. Method: This study investigates the automatic classification of Selonding gamelan music using a Convolutional Neural Network (CNN). The dataset consists of 10 traditional Selonding compositions. Recordings were segmented into fixed 15-second excerpts, converted to WAV, normalized, and transformed into time–frequency features using two approaches: Mel-Frequency Cepstral Coefficients (MFCC) and Constant-Q Transform (CQT). A CNN-based classifier was trained and evaluated using 5-fold cross-validation for each feature representation. Results: The MFCC-based model achieved stable high performance, with mean accuracy of 94.67% (±2.11%), mean precision of 94.97% (±1.90%), mean recall of 94.67% (±2.11%), and mean F1-score of 94.63% (±2.12%) across folds. In contrast, the CQT-based model performed notably worse, reaching only 58.04% mean accuracy and 53.28% mean F1-score, with large variance across folds. These results indicate that MFCC features capture the discriminative timbral characteristics of Selonding more effectively than CQT under the current experimental setting. Conclusion: Overall, the findings show that a CNN trained on MFCC features can reliably distinguish Selonding compositions using only short (15-second) audio segments, despite limited data. This suggests that deep learning is a feasible strategy for indexing, retrieval, and long-term preservation of Balinese gamelan repertoires.

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