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Electrical Engineering and Systems Science > Audio and Speech Processing

arXiv:2203.16165 (eess)
[Submitted on 30 Mar 2022 (v1), last revised 4 May 2022 (this version, v2)]

Title:Symbolic music generation conditioned on continuous-valued emotions

Authors:Serkan Sulun, Matthew E. P. Davies, Paula Viana
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Abstract: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.
Comments: Published in IEEE Access
Subjects: Audio and Speech Processing (eess.AS); Artificial Intelligence (cs.AI); Multimedia (cs.MM)
Cite as: arXiv:2203.16165 [eess.AS]
  (or arXiv:2203.16165v2 [eess.AS] for this version)
  https://doi.org/10.48550/arXiv.2203.16165
Journal reference: volume:10, year:2022, pages:44617-44626
Related DOI: https://doi.org/10.1109/ACCESS.2022.3169744

Submission history

From: Serkan Sulun [view email]
[v1] Wed, 30 Mar 2022 09:38:09 UTC (812 KB)
[v2] Wed, 4 May 2022 12:04:03 UTC (7,883 KB)
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