Performance Comparison of KNN and CNN in Classifying Balinese Gangsa Instrument Tones
DOI:
10.33395/sinkron.v8i4.14019Keywords:
Audio Signal Processing, Traditional Music Instrument, Tones Identification, Chroma Features, MFCCsAbstract
Balinese traditional music, particularly the Gamelan Gangsa, represents a unique aspect of Indonesia’s cultural heritage. Despite its cultural significance, the study and teaching of this instrument face challenges, particularly in tone standardization and the availability of effective learning tools. This research addresses these challenges by exploring the application of Artificial Intelligence (AI) technologies specifically K-Nearest Neighbors (KNN) and Convolutional Neural Networks (CNN) in the identification and classification of Gamelan Gangsa tones. The study involved the creation of a dataset comprising audio recordings of the instrument, followed by the development and evaluation of KNN and CNN models. The results indicate that KNN, with an accuracy of 90%, outperformed CNN, which achieved an accuracy of 85%. The findings suggest that KNN is particularly effective in distinguishing subtle tonal differences, making it a valuable tool for supporting traditional music education. This research not only contributes to the technical understanding of Gamelan Gangsa’s acoustic characteristics but also underscores the potential of AI in cultural preservation. The development of AI-based tone identification systems can facilitate the teaching and learning of traditional music, ensuring its transmission to future generations. The study serves as a foundation for further exploration into the integration of AI technologies with cultural heritage, demonstrating how modern innovations can enhance the appreciation and understanding of traditional arts.
Downloads
References
Abdul, Z. K., & Al-Talabani, A. K. (2022). Mel frequency cepstral coefficient and its applications: A review. IEEE Access, 10, 122136–122158.
Ahmad, I., Rahmanto, Y., Pratama, D., & Borman, R. I. (2021). Development of augmented reality application for introducing tangible cultural heritages at the lampung museum using the multimedia development life cycle. Ilk. J. Ilm, 13(2), 187–194.
Birajdar, G. K., & Patil, M. D. (2020). Speech/music classification using visual and spectral chromagram features. Journal of Ambient Intelligence and Humanized Computing, 11(1), 329–347.
Boestad, A., & Rudberg, F. (2021). Piezoelectric Guitar Tuner.
Chen, Q., Wu, Z., Zhong, Q., & Li, Z. (2022). Heart Sound Classification Based on Mel-Frequency Cepstrum Coefficient Features and Multi-Scale Residual Recurrent Neural Networks. Journal of Nanoelectronics and Optoelectronics, 17(8), 1144–1153.
Das, J. K., Ghosh, A., Pal, A. K., Dutta, S., & Chakrabarty, A. (2020). Urban sound classification using convolutional neural network and long short term memory based on multiple features. 2020 Fourth International Conference On Intelligent Computing in Data Sciences (ICDS), 1–9.
Demir, F., Abdullah, D. A., & Sengur, A. (2020). A new deep CNN model for environmental sound classification. IEEE Access, 8, 66529–66537.
Kulkarni, U., Kaushik, S., Lobo, L., & Sonkusare, R. (2020). Comparative study of digital signal processing techniques for tuning an acoustic guitar. 2020 7th International Conference on Smart Structures and Systems (ICSSS), 1–5.
Kumar, B. D., Kushwaha, A., Kumar, A., & Agarwal, A. (2021). Design & Implementation of Digital Guitar Tuner Using MATLAB. 2021 International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), 547–549.
Li, W. (2022). Analysis of piano performance characteristics by deep learning and artificial intelligence and its application in piano teaching. Frontiers in Psychology, 12, 751406.
Liu, M., & Huang, J. (2021). Piano playing teaching system based on artificial intelligence–design and research. Journal of Intelligent & Fuzzy Systems, 40(2), 3525–3533.
McGraw, A. C. (2008). Different temporalities: the time of Balinese gamelan. Yearbook for Traditional Music, 40, 136–162.
Mukti, R. W. T., & Fathurrahman, M. F. (2023). PENGEMBANGAN MEDIA PEMBELAJARAN ARTSTEPS UNTUK MENINGKATKAN HASIL BELAJAR SENI MUSIK: Materi Alat Musik Tradisional Siswa Kelas IV SD. Jurnal Pendidikan Teknologi Informasi (JUKANTI), 6(2), 329–341.
Parker, M., & Spennemann, D. H. R. (2022). Classifying sound: a tool to enrich intangible heritage management. Acoustics Australia, 50(1), 23–39.
Powers, D. M. W. (2020). Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. ArXiv Preprint ArXiv:2010.16061.
Prabavathy, S., Rathikarani, V., & Dhanalakshmi, P. (2020). Classification of Musical Instruments using SVM and KNN. International Journal of Innovative Technology and Exploring Engineering, 9(7), 1186–1190.
Pradana, K. A. W., & Suherta, I. W. (2023). The Musicality of Gamelan Gong Kebyar Mepacek as a North Bali Traditional Music Identity. Randwick International of Social Science Journal, 4(2), 241–253.
Rosmiati, T., Bahari, J. P., & Yuliyanti, A. (2024). Pola Barisan Dalam Alat Musik Gangsa Daerah Bali. Cerdika: Jurnal Ilmiah Indonesia, 4(2), 102–108.
Su, Y., Zhang, K., Wang, J., Zhou, D., & Madani, K. (2020). Performance analysis of multiple aggregated acoustic features for environment sound classification. Applied Acoustics, 158, 107050.
Sudipa, I. G. I., Aditama, P. W., & Yanti, C. P. (2022). Developing Augmented Reality Lontar Prasi Bali as an E-learning Material to Preserve Balinese Culture. J. Wirel. Mob. Networks Ubiquitous Comput. Dependable Appl., 13(4), 169–181.
Tiwari, S., Sapra, V., & Jain, A. (2020). Heartbeat sound classification using Mel-frequency cepstral coefficients and deep convolutional neural network. In Advances in Computational Techniques for Biomedical Image Analysis (pp. 115–131). Elsevier.
Tsalera, E., Papadakis, A., & Samarakou, M. (2020). Monitoring, profiling and classification of urban environmental noise using sound characteristics and the KNN algorithm. Energy Reports, 6, 223–230.
Yacouby, R., & Axman, D. (2020). Probabilistic extension of precision, recall, and f1 score for more thorough evaluation of classification models. Proceedings of the First Workshop on Evaluation and Comparison of NLP Systems, 79–91.
Downloads
|
How to Cite
Issue
Section
License
Copyright (c) 2024 I Gede Putra Mas Yusadara, Ni Made Rai Masita Dewi, I Gede Bintang Arya Budaya
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Muhammad Khoiruddin harahap
Google Scholar 
Moraref
PKP Index
Garuda Kemdikbud
Indonesia OneSearch
OCLC Worldcat
Dimensions
Index Copernicus
Scilit
