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Music accompaniment generation is a crucial aspect in the compositionprocess. Deep neural networks have made significant strides in this field, butit remains a challenge for AI to effectively incorporate human emotions tocreate beautiful accompaniments. Existing models struggle to effectivelycharacterize human emotions within neural network models while composing music.To address this issue, we propose the use of an easy-to-represent emotion flowmodel, the Valence/Arousal Curve, which allows for the compatibility ofemotional information within the model through data transformation and enhancesinterpretability of emotional factors by utilizing a Variational Autoencoder asthe model structure. Further, we used relative self-attention to maintain thestructure of the music at music phrase level and to generate a richeraccompaniment when combined with the rules of music theory.

Emotion-Guided Music Accompaniment Generation Based on Variational Autoencoder