Sound detection of gamelan musical instruments using teachable machine

Gamelan is an instrument of musical expression that has an aesthetic function related to social, moral, and spiritual values. Gamelan consists of a variety of musical instruments that have a unique sound. In this study, the sound detection of nine gamelan musical instruments was carried out using a teachable machine. The gamelan musical instruments detected included gong, kenong, saron, bonang, gambang, kendang, flute, siter, and rebab. The algorithm used is CNN. The CNN algorithm has a fairly good performance for the sound detection process. The test results of the built model show an "acc" value of 25 ranging from 0.99 to 1, which indicates that the model achieves an accuracy rate of 99% to 100% on the training dataset. At the same time, "test accuracy" refers to a measure of the model's effectiveness in predicting data it has not encountered during training. The "test accuracy" score varied from 0. 83, which shows that the validation data has an accuracy of 83%.

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Publication Details

Journal
Journal of Soft Computing Exploration
Published
2025-06-24
DOI
https://doi.org/10.52465/joscex.v6i2.576
Primary Topic
Music Technology and Sound Studies
Type
article
Field-Weighted Citation Impact
0.00
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article

Sound detection of gamelan musical instruments using teachable machine

Latjuba Sofyana STT, Yessi Yunitasari, Inung Diah Kurniawati, Moch Yusuf Asyhari
Journal of Soft Computing Exploration
Music Technology and Sound Studies
article

Sound detection of gamelan musical instruments using teachable machine

Latjuba Sofyana STT, Yessi Yunitasari, Inung Diah Kurniawati, Moch Yusuf Asyhari
article en

Abstract

Gamelan is an instrument of musical expression that has an aesthetic function related to social, moral, and spiritual values. Gamelan consists of a variety of musical instruments that have a unique sound. In this study, the sound detection of nine gamelan musical instruments was carried out using a teachable machine. The gamelan musical instruments detected included gong, kenong, saron, bonang, gambang, kendang, flute, siter, and rebab. The algorithm used is CNN. The CNN algorithm has a fairly good performance for the sound detection process. The test results of the built model show an "acc" value of 25 ranging from 0.99 to 1, which indicates that the model achieves an accuracy rate of 99% to 100% on the training dataset. At the same time, "test accuracy" refers to a measure of the model's effectiveness in predicting data it has not encountered during training. The "test accuracy" score varied from 0. 83, which shows that the validation data has an accuracy of 83%.

Journal of Soft Computing ExplorationVol. 6(2)
Universitas PGRI Madiun (ID)
Openalex Percentile: Top 87%
Music Technology and Sound Studies
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