Projects per year
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.
|Number of pages||10|
|Publication status||Published - 17 Mar 2019|
|Event||2019 AES International Conference on Immersive and Interactive Audio: Creating the Next Dimension of Sound Experience - York, United Kingdom|
Duration: 27 Mar 2019 → 29 Mar 2019
|Conference||2019 AES International Conference on Immersive and Interactive Audio: Creating the Next Dimension of Sound Experience|
|Period||27/03/19 → 29/03/19|
Bibliographical note© 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.
- 1 Finished
The Digital Creativity Hub
Cowling, P. I., Austin, J., Cairns, P. A., Holliman, N. S., Hook, J. D., Marsden, E., Murphy, D. T., Petrie, H., Reed, D. J., Richards, J. D., Ursu, M., Wade, A., Baier, H., Beale, G., Block, F. O., Deterding, C. S., Devlin, S., Drachen, A., Kasprowicz, R. E., Smith, D. P. & Williams, D. A. H.
1/10/15 → 30/09/22
Project: Research project (funded) › Research