| Autor: |
Rendra Soekarta, Suhardi Aras, Ahmad Nur Aswad |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), Vol 7, Iss 5, Pp 1205-1210 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2580-0760 |
| DOI: |
10.29207/resti.v7i5.5319 |
| Popis: |
Playing music through a digital platform that has a large database of songs requires automated classification of music genres, highlighting the need to develop a model for music genre classification that is more efficient and accurate. This study evaluated the hyperparameters in the music genre classification process using CNN in the GTZAN dataset with 30-second duration data optimized using MFCC feature extraction. The model that is formed with a time of 3 (three) seconds classifies music genres in the first 3 seconds of music. This model has a high potential for error because the first 3 seconds of initial music are varied and cannot be used as a benchmark in determining music genres. This study performed hyperparameters on batch size, epoch, and split data set variables with various scenarios. The highest precision result was obtained at 72% with a data split of 85%:15%, 32 batch sizes, and 500 epochs. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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