AI and Automatic Music Generation for Mindfulness

TY - CONF

T1 - AI and Automatic Music Generation for Mindfulness

AU - Williams, Duncan

AU - Hodge, Victoria J.

AU - Gega, Lina

AU - Murphy, Damian

AU - Cowling, Peter I.

AU - Drachen, Anders

N1 - © 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.

PY - 2019/3/17

Y1 - 2019/3/17

N2 - 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.

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.

UR - http://www.scopus.com/inward/record.url?scp=85067103170&partnerID=8YFLogxK

M3 - Paper

AN - SCOPUS:85067103170

T2 - 2019 AES International Conference on Immersive and Interactive Audio: Creating the Next Dimension of Sound Experience

Y2 - 27 March 2019 through 29 March 2019

ER -