Multimodal Music Emotion Recognition with Hierarchical Cross-Modal Attention Network

Abstract

Computational music emotion recognition is to recognize the emotional content in music tracks. In computational music emotion recognition studies, researchers have paid close attention to the audio content of the music tracks. Although lyrics content and music context contribute greatly to the perceived emotion, these kinds of emotional information are usually ignored. Based on this finding, we propose a multimodal music emotion recognition method jointly predicting the valence and arousal values by combining the audio, lyrics, track name, and artist of a given track. Audio features, lyrics features and context features are extracted separately and fused by a cross-modal attention mechanism, forming a hierarchical structure. Our proposed model outperforms two baselines by a large margin and achieves state-of-the-art performance on two public datasets.