AI and Automatic Music Generation for Mindfulness

Williams D, Hodge V, Gega L, Murphy D, Cowling P, Drachen A. AI and Automatic Music Generation for Mindfulness. In: AES Conference: 2019 AES International Conference on Immersive and Interactive Audio. Audio Engineering Society; 2019. Paper 84. Available from: https://aes.org/publications/elibrary-page/?id=20439

@inproceedings{Williams2019_20439,
  author = {Williams, Duncan and Hodge, Victoria and Gega, Lina and Murphy, Damian and Cowling, Peter and Drachen, Anders},
  title = {{AI and Automatic Music Generation for Mindfulness}},
  booktitle = {AES Conference: 2019 AES International Conference on Immersive and Interactive Audio},
  note = {Paper 84},
  year = {2019},
  month = mar,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=20439}
}

TY  - CPAPER
TI  - AI and Automatic Music Generation for Mindfulness
AU  - Williams, Duncan
AU  - Hodge, Victoria
AU  - Gega, Lina
AU  - Murphy, Damian
AU  - Cowling, Peter
AU  - Drachen, Anders
T2  - AES Conference: 2019 AES International Conference on Immersive and Interactive Audio
M1  - Paper 84
PY  - 2019
DA  - 2019/03/06
UR  - https://aes.org/publications/elibrary-page/?id=20439
PB  - Audio Engineering Society
LA  - en
AB  - 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.
ER  -