An Efficient Bi-Modal Fusion Framework for Music Emotion Recognition

Current methods for Music Emotion Recognition (MER) face challenges in effectively extracting features sensitive to emotions, especially those rich in temporal detail. Moreover, the narrow scope of music-related modalities impedes data integration from multiple sources, while including multiple modalities often leads to redundant information, which can degrade performance. To address these issues, we propose a lightweight framework for music emotion recognition that improves the extraction of features that are both sensitive to emotions and rich in temporal information and that integrates data from both audio and MIDI modalities while minimizing redundancy. Our approach involves developing two innovative unimodal encoders to learn embeddings from audio and MIDI-like features. Additionally, we introduce a Bi-modal Fusion Attention Model (BFAM) that integrates features from low-level to high-level semantic information across different modalities. Experimental evaluations on the EMOPIA and VGMIDI datasets show that our unimodal networks achieve accuracies that are 6.1% and 4.4% higher than baseline algorithms for MIDI and audio on the EMOPIA dataset, respectively. Furthermore, our BFAM achieves a 15.2% improvement in accuracy over the baseline, reaching 82.2%, which underscores its effectiveness for bi-modal MER applications.