Williams, Duncan, Hodge, Victoria J. orcid.org/0000-0002-2469-0224, Gega, Lina orcid.org/0000-0003-2902-9256 et al. (3 more authors) (2019) AI and Automatic Music Generation for Mindfulness. In: 2019 AES International Conference on Immersive and Interactive Audio: Creating the Next Dimension of Sound Experience, 27-29 Mar 2019.
Abstract
This paper presents an architecture for the creation of emotionally congruent music using machine learning aided sound synthesis. Our system can generate a small corpus of music using Hidden Markov Models; we can label the pieces with emotional tags using data elicited from questionnaires. This produces a corpus of labelled music underpinned by perceptual evaluations. We then analyse participant’s galvanic skin response (GSR) while listening to our generated music pieces and the emotions they describe in a questionnaire conducted after listening. These analyses reveal that there is a direct correlation between the calmness/scariness of a musical piece, the users’ GSR reading and the emotions they describe feeling. From these, we will be able to estimate an emotional state using biofeedback as a control signal for a machine-learning algorithm, which generates new musical structures according to a perceptually informed musical feature similarity model. Our case study suggests various applications including in gaming, automated soundtrack generation, and mindfulness.
Metadata
Item Type: | Conference or Workshop Item |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 Audio Engineering Society. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details. |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) The University of York > Faculty of Sciences (York) > Hull York Medical School (York) The University of York > Faculty of Sciences (York) > Electronic Engineering (York) |
Funding Information: | Funder Grant number EPSRC EP/M023265/1 |
Depositing User: | Pure (York) |
Date Deposited: | 22 Jan 2019 14:00 |
Last Modified: | 21 Jan 2025 18:29 |
Status: | Published |
Refereed: | Yes |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:141387 |