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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Valdera, Fauzan
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Computer Science & IT Engineering

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Music genre classification using Inception-ResNet architecture Valdera, Fauzan; Arifin, Ajib Setyo
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp3300-3310

Abstract

Music genres help categorize music but lack strict boundaries, emerging from interactions among public, marketing, history, and culture. With Spotify hosting over 80 million tracks, organizing digital music is challenging due to the sheer volume and diversity. Automating music genre classification aids in managing this vast array and attracting customers. Recently, convolutional neural networks (CNNs) have been used for their ability to extract hierarchical features from images, applicable to music through spectrograms. This study introduces the Inception-ResNet architecture for music genre classification, significantly improving performance with 94.10% accuracy, precision of 94.19%, recall of 94.10%, F1-score of 94.08%, and 149,418 parameters on the GTZAN dataset, showcasing its potential in efficiently managing and categorizing large music databases.
Co-Authors Ajib Setyo Arifin