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D. Williams, V. Hodge, L. Gega, D. Murphy, P. Cowling, and A. Drachen, "AI and Automatic Music Generation for Mindfulness," Paper 84, (2019 March.). doi: D. Williams, V. Hodge, L. Gega, D. Murphy, P. Cowling, and A. Drachen, "AI and Automatic Music Generation for Mindfulness," Paper 84, (2019 March.). doi:
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.

@article{williams2019ai,
author={williams, duncan and hodge, victoria and gega, lina and murphy, damian and cowling, peter and drachen, anders},
journal={journal of the audio engineering society},
title={ai and automatic music generation for mindfulness},
year={2019},
volume={},
number={},
pages={},
doi={},
month={march},} @article{williams2019ai,
author={williams, duncan and hodge, victoria and gega, lina and murphy, damian and cowling, peter and drachen, anders},
journal={journal of the audio engineering society},
title={ai and automatic music generation for mindfulness},
year={2019},
volume={},
number={},
pages={},
doi={},
month={march},
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.},}

TY - paper
TI - AI and Automatic Music Generation for Mindfulness
SP - EP -
AU - Williams, Duncan
AU - Hodge, Victoria
AU - Gega, Lina
AU - Murphy, Damian
AU - Cowling, Peter
AU - Drachen, Anders
PY - 2019
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - March 2019 TY - paper
TI - AI and Automatic Music Generation for Mindfulness
SP - EP -
AU - Williams, Duncan
AU - Hodge, Victoria
AU - Gega, Lina
AU - Murphy, Damian
AU - Cowling, Peter
AU - Drachen, Anders
PY - 2019
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - March 2019
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.