Tempo Recognition of Kendhang Instruments Using Hybrid Feature Extraction

Muljono; Pulung Nurtantio  Andono; Sari Ayu  Wulandari; Harun Al  Azies; Muhammad  Naufal

doi:10.6180/jase.202403_27(3).0004

Tempo Recognition of Kendhang Instruments Using Hybrid Feature Extraction

Computer Science and Information Engineering

Muljono¹This email address is being protected from spambots. You need JavaScript enabled to view it., Pulung Nurtantio Andono¹, Sari Ayu Wulandari², Harun Al Azies¹, Muhammad Naufal¹

¹Department of Informatics Engineering, Universitas Dian Nuswantoro, Semarang 50131, Indonesia

²Department of Electrical Engineering, Universitas Dian Nuswantoro, Semarang 50131, Indonesia

Received: January 16, 2023
Accepted: June 14, 2023
Publication Date: August 4, 2023

Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202403_27(3).0004

This article is the result of research on Gamelan instruments that examines from a technological perspective what is rarely done nowadays, through kendhang tempo recognition by proposing three classification modeling schemes. The proposed scheme is a new approach to kendhang tempo classification, using kendhang sound converted to image-based features via Mel spectrogram, then features are extracted from the image with Visual Geometry Group (VGG)-19 before incorporating the method K-Nearest Neighbour (K-NN) as a classification method. Based on the experimental results that have been obtained, modeling using the 3rd scheme, namely two-phase feature extraction from the Mel spectrogram image as the first phase and the second phase of VGG-19 with classification using K-NN has an advantage in accuracy (99.6%) of implementing Kendhang tempo recognition correctly and the average achievement of the fastest training processing time was 3.37 seconds compared to the 1st scheme with an accuracy of 94% and an average model training process time of 16.4 seconds and the 2nd scheme with a model accuracy of 98% and the average time to complete the model training process the longest is 6228.6 seconds.

Keywords: Features Extraction, K-Nearest Neighbour, Mel spectrogram, Sound Recognition, VGG-19

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