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. 2023 Jun 6:14:1158172.
doi: 10.3389/fpsyg.2023.1158172. eCollection 2023.

AffectMachine-Classical: a novel system for generating affective classical music

Kat R Agres #  1   2 , Adyasha Dash #  1 , Phoebe Chua  3
Affiliations

AffectMachine-Classical: a novel system for generating affective classical music

Kat R Agres et al. Front Psychol. .

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.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
(A) Linear regression between parameter settings and average valence ratings. (B) Linear regression between parameter settings and average arousal ratings. Error bars display standard error.
Figure 2
Figure 2
Average (interpolated) valence and arousal ratings as a function of the valence and arousal parameters. Vertical color bars represent the colors corresponding to different values of normalized average ratings over the range of 0.1–0.9.
Figure 3
Figure 3
(A) Average valence rating and linear regression for MT group. (B) Average arousal rating and linear regression for MT group. (C) Average valence rating and linear regression for NMT group. (D) Average arousal rating and linear regression for NMT group. Error bars depict standard error.
See this image and copyright information in PMC

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