Discerning Music Genres: Exploring Neural Network Architectures for Automated Classification

This study investigates the application of various artificial neural network (ANN) architectures for music genre classification, focusing on the evolution of models and their performance on the GTZAN dataset. Through experimentation with fully connected networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and Transformer-based architectures, we analyze their strengths and limitations in genre classification. The results demonstrate that hyperparameter tuning, dataset diversity, and model selection significantly impact classification performance. Although CNNs, RNNs, and gated recurrent units (GRUs) achieve more precision than 90% in evaluation tests, the addition of Transformer layers does not consistently improve classification accuracy and may exacerbate challenges in certain genres. These results underscore the importance of carefully considering model architecture and dataset characteristics in music genre classification. Future research should focus on developing extensive, multi-rater verified datasets to enhance classification performance and model robustness in music genre classification tasks.