Symbolic music generation conditioned on continuous-valued emotions

Sulun, Serkan; Davies, Matthew E. P.; Viana, Paula

http://hdl.handle.net/10400.22/21681

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Authors

Sulun, Serkan

Davies, Matthew E. P.

Viana, Paula

Abstract(s)

In this paper we present a new approach for the generation of multi-instrument symbolic music driven by musical emotion. The principal novelty of our approach centres on conditioning a state-of-the-art transformer based on continuous-valued valence and arousal labels. In addition, we provide a new large-scale dataset of symbolic music paired with emotion labels in terms of valence and arousal. We evaluate our approach in a quantitative manner in two ways, first by measuring its note prediction accuracy, and second via a regression task in the valence-arousal plane. Our results demonstrate that our proposed approaches outperform conditioning using control tokens which is representative of the current state of the art.

Keywords

Deep Learning Symbolic Music Generation Emotion-Based Music Generation Transformers

URI

http://hdl.handle.net/10400.22/21681

Citation

S. Sulun, M. E. P. Davies and P. Viana, "Symbolic Music Generation Conditioned on Continuous-Valued Emotions," in IEEE Access, vol. 10, pp. 44617-44626, 2022, doi: 10.1109/ACCESS.2022.3169744.