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We consider the task of multimodal music mood prediction based on the audiosignal and the lyrics of a track. We reproduce the implementation oftraditional feature engineering based approaches and propose a new model basedon deep learning. We compare the performance of both approaches on a databasecontaining 18,000 tracks with associated valence and arousal values and showthat our approach outperforms classical models on the arousal detection task,and that both approaches perform equally on the valence prediction task. Wealso compare the a posteriori fusion with fusion of modalities optimizedsimultaneously with each unimodal model, and observe a significant improvementof valence prediction. We release part of our database for comparison purposes.

Music Mood Detection Based On Audio And Lyrics With Deep Neural Net