Hyperparameter Optimization of CNN Classifier for Music Genre Classification

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 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.