Adaptive Feature Fusion Gate and Gated Channel-Spatial Attention in CNN-Transformer Models for Music Genre Classification

Abstract

With the rapid growth of music data, automatic music genre classification has become a critical task in music information retrieval. Traditional methods based on handcrafted features are increasingly inadequate when handling large-scale analysis. This paper proposes the Convolutional Neural Network-Gated Transformer Network (CT-GateNet), a hybrid architecture that integrates a gated channel-spatial attention mechanism with an adaptive feature fusion gating mechanism to achieve discriminative feature learning and efficient feature integration. To mitigate data scarcity, a data augmentation strategy based on a denoising diffusion probabilistic model is introduced. Experiments are conducted on three public music genre datasets: GTZAN, FMA-SMALL and FMA-Medium. The method achieves classification accuracies of 98.72%, 89.42%, and 69.07% on GTZAN, FMA-SMALL and FMA-Medium, respectively, demonstrating outstanding performance and robust generalization capabilities. These results validate CT-GateNet’s effectiveness in music genre classification and provide valuable insights for audio classification research.