Graph-Based Multimodal Music Mood Classification in Discriminative Latent Space

Abstract

Automatic music mood classification is an important and challenging problem in the field of music information retrieval (MIR) and has attracted growing attention from variant research areas. In this paper, we proposed a novel multimodal method for music mood classification that exploits the complementarity of the lyrics and audio information of music to enhance the classification accuracy. We first extract descriptive sentence-level lyrics and audio features from the music. Then, we project the paired low-level features of two different modalities into a learned common discriminative latent space, which not only eliminates between modality heterogeneity, but also increases the discriminability of the resulting descriptions. On the basis of the latent representation of music, we employ a graph learning based multi-modal classification model for music mood, which takes the cross-modality similarity between local audio and lyrics descriptions of music into account for effective exploitation of correlations between different modalities. The acquired predictions of mood category for every sentence of music are then aggregated by a simple voting scheme. The effectiveness of the proposed method has been demonstrated in the experiments on a real dataset composed of more than 3,000 min of music and corresponding lyrics.