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Sunderland Repository records the research produced by the University of Sunderland including practice-based research and theses.

Emotion-driven Motivic Development using Diffusion Models

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Mo, Ronald K. (2023) Emotion-driven Motivic Development using Diffusion Models. In: CHIME Annual One-day Music and HCI Workshop, 04 Dec 2023, The Open University, Milton Keynes, UK. (Submitted)

Item Type: Conference or Workshop Item (Other)


Denoising diffusion probabilistic models, or diffusion models, have been successfully used for generating images, audio, and music. This work aims to investigate the potential of employing diffusion models to develop a motif composed by human composers to arouse specific emotions. To achieve this, a dataset consisting of melodies and their corresponding emotion label is constructed for training the diffusion model. The model is conditioned on the user-generated motif and a label displaying the desired emotion category, which opens up an opportunity for human composers to collaborate with computer technology in the field of music composition.

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Depositing User: Ronald Mo


Item ID: 17021
Official URL:

Users with ORCIDS

ORCID for Ronald K. Mo: ORCID iD

Catalogue record

Date Deposited: 21 Nov 2023 10:39
Last Modified: 21 Nov 2023 10:39


Author: Ronald K. Mo ORCID iD

University Divisions

Faculty of Technology > School of Computer Science


Computing > Artificial Intelligence
Computing > Human-Computer Interaction
Performing Arts > Music

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