MCGC-Net: Multi-Scale Controllable Graph Convolutional Network on Music Emotion Recognition

This paper introduces a novel dynamic graph learning approach for frequency graphs, underpinned by a suite of baseline methodologies and the Multi-scale Controllable Graph Convolutional Network (MCGC-net), a multi-channel dynamic graph learning framework. This approach addresses the limitations of temporal graphs in capturing global and local correlations in music signals for music emotion recognition tasks. Frequency graphs integrate timbre and chroma audio features to capture both local and global correlations in music, enhancing emotion classification and segmentation. The graph feature extractor module improves the dissemination of hierarchical feature information through control units and enhances the depiction of remote node attributes via shortcut connections. Additionally, the multi-channel feature extractor module expands the receptive field, enhancing inter-channel feature extraction. The integration of these modules enables the model to proficiently extract musical frequency features, uncovering latent correlations among various frequencies and their impact on emotion recognition. Empirical validation across the 1000songs, PMEmo, and Memo2496 datasets demonstrates the strategy's efficacy, with MCGC-net achieving classification accuracies of 79.03% for Arousal and 70.05% for Valence on 1000songs; 85.81% and 75.42% on PMEmo; and 79.52% and 78.61% on Memo2496, respectively. Complementary ablation studies and T-order robustness assessments further validate the unique contributions of model components and the performance enhancements provided by shortcut connections.