Hyperparameter Optimization of CNN Classifier for Music Genre Classification

Hyperparameter Optimization of CNN Classifier for Music Genre Classification

music genre classification; deep learning; GTZAN dataset

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 the CNN on the GTZAN dataset with 30-second duration
data optimized using the 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 is
varied and cannot be used as a benchmark in determining music genres. This study performed hyperparameters on batch size,
epoch, and split dataset variables with various scenarios. The highest accuracy result was obtained at 72% with a data split
of 85%:15%, 32 batch size,s and 500 epochs.

Rendra Soerkarta, Suhardi Aras, Ahmad Nur Aswad

http://jurnal.iaii.or.id

Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association

October 2023

Sri Wahyuni

ISSN Media Electronic: 2580-0760

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