Tempo Recognition of Kendhang Instruments Using Hybrid Feature Extraction
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, us...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Tamkang University Press
2024-01-01
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| Series: | Journal of Applied Science and Engineering |
| Subjects: | |
| Online Access: | http://jase.tku.edu.tw/articles/jase-202403-27-3-0004 |
| _version_ | 1797338812215459840 |
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| author | Muljono Pulung Nurtantio Andono Sari Ayu Wulandari Harun Al Azies Muhammad Naufal |
| author_facet | Muljono Pulung Nurtantio Andono Sari Ayu Wulandari Harun Al Azies Muhammad Naufal |
| author_sort | Muljono |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T09:35:42Z |
| format | Article |
| id | doaj.art-4b609e99a121492999eb25ac907eb6db |
| institution | Directory Open Access Journal |
| issn | 2708-9967 2708-9975 |
| language | English |
| last_indexed | 2024-03-08T09:35:42Z |
| publishDate | 2024-01-01 |
| publisher | Tamkang University Press |
| record_format | Article |
| series | Journal of Applied Science and Engineering |
| spelling | doaj.art-4b609e99a121492999eb25ac907eb6db2024-01-30T08:19:50ZengTamkang University PressJournal of Applied Science and Engineering2708-99672708-99752024-01-012732177219010.6180/jase.202403_27(3).0004Tempo Recognition of Kendhang Instruments Using Hybrid Feature ExtractionMuljono0Pulung Nurtantio Andono1Sari Ayu Wulandari2Harun Al Azies3Muhammad Naufal4Department of Informatics Engineering, Universitas Dian Nuswantoro, Semarang 50131, IndonesiaDepartment of Informatics Engineering, Universitas Dian Nuswantoro, Semarang 50131, IndonesiaDepartment of Electrical Engineering, Universitas Dian Nuswantoro, Semarang 50131, IndonesiaDepartment of Informatics Engineering, Universitas Dian Nuswantoro, Semarang 50131, IndonesiaDepartment of Informatics Engineering, Universitas Dian Nuswantoro, Semarang 50131, IndonesiaThis 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.http://jase.tku.edu.tw/articles/jase-202403-27-3-0004features extractionk-nearest neighbourmel spectrogramsound recognitionvgg-19 |
| spellingShingle | Muljono Pulung Nurtantio Andono Sari Ayu Wulandari Harun Al Azies Muhammad Naufal Tempo Recognition of Kendhang Instruments Using Hybrid Feature Extraction Journal of Applied Science and Engineering features extraction k-nearest neighbour mel spectrogram sound recognition vgg-19 |
| title | Tempo Recognition of Kendhang Instruments Using Hybrid Feature Extraction |
| title_full | Tempo Recognition of Kendhang Instruments Using Hybrid Feature Extraction |
| title_fullStr | Tempo Recognition of Kendhang Instruments Using Hybrid Feature Extraction |
| title_full_unstemmed | Tempo Recognition of Kendhang Instruments Using Hybrid Feature Extraction |
| title_short | Tempo Recognition of Kendhang Instruments Using Hybrid Feature Extraction |
| title_sort | tempo recognition of kendhang instruments using hybrid feature extraction |
| topic | features extraction k-nearest neighbour mel spectrogram sound recognition vgg-19 |
| url | http://jase.tku.edu.tw/articles/jase-202403-27-3-0004 |
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