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Publicação
Symbolic music generation conditioned on continuous-valued emotions
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Orientador(es)
Resumo(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.
Descrição
Palavras-chave
Deep Learning Symbolic Music Generation Emotion-Based Music Generation Transformers
Contexto Educativo
Citação
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.
Editora
IEEE