@article {2023,
	title = {AffectMachine-Classical: a novel system for generating affective classical music},
	journal = {Frontiers in Psychology 14:1158172. doi: 10.3389/fpsyg.2023.1158172},
	volume = {14:1158172},
	year = {2023},
	month = {06/2023},
	pages = {1-13},
	type = {Journal article},
	abstract = {This work introduces a new music generation system, called AffectMachine-Classical, that is capable of generating affective Classic music in real-time. AffectMachine was designed to be incorporated into biofeedback systems (such as brain-computer-interfaces) to help users become aware of, and ultimately mediate, their own dynamic affective states. That is, this system was developed for music-based MedTech to support real-time emotion self-regulation in users. We provide an overview of the rule-based, probabilistic system architecture, describing the main aspects of the system and how they are novel. We then present the results of a listener study that was conducted to validate the ability of the system to reliably convey target emotions to listeners. The findings indicate that AffectMachine-Classical is very effective in communicating various levels of Arousal (R2 = 0.96) to listeners, and is also quite convincing in terms of Valence (R2 = 0.90). Future work will embed AffectMachine-Classical into biofeedback systems, to leverage the efficacy of the affective music for emotional wellbeing in listeners.},
	keywords = {affective computing, algorithmic composition, automatic music generation system, emotion regulation, listener validation study, Music MedTech},
	doi = {doi.org/10.3389/fpsyg.2023.1158172},
	url = {https://www.frontiersin.org/articles/10.3389/fpsyg.2023.1158172/full},
	author = {Kat R. Agres and Adyasha Dash and Phoebe Chua}
}
