Hyperparameter Optimization of CNN Classifier for Music Genre Classification

Rendra Soekarta; Suhardi Aras; Ahmad Nur Aswad

doi:10.29207/resti.v7i5.5319

Rendra Soekarta Universitas Muhammadiyah Sorong
Suhardi Aras Universitas Muhammadiyah Sorong
Ahmad Nur Aswad Universitas Muhammadiyah Sorong

DOI: https://doi.org/10.29207/resti.v7i5.5319

Keywords: deep learning, music genre classification, GTZAN dataset

Abstract

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.

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