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 ($R^2 = .96$) to listeners, and is also quite convincing in terms of Valence (R^2 = .90). Future work will embed AffectMachine-Classical into biofeedback systems, to leverage the efficacy of the affective music for emotional well-being in listeners.

翻译：本工作介绍一种名为AffectMachine-Classical的新型音乐生成系统，该系统能够实时生成富有情感的古典音乐。AffectMachine的设计初衷是融入生物反馈系统（如脑机接口），以帮助用户感知并最终调节自身的动态情感状态。也就是说，该系统是为基于音乐技术的医疗设备所开发，支持用户进行实时情绪自我调节。我们概述了基于规则的概率系统架构，描述了该系统的主要创新特性。随后展示了听众研究结果，该研究旨在验证系统向听众可靠传递目标情感的能力。结果表明，AffectMachine-Classical在向听众传达不同水平的唤醒度方面非常有效（$R^2 = .96$），在效价维度上也具有相当高的说服力（$R^2 = .90$）。未来工作将把AffectMachine-Classical嵌入生物反馈系统，以利用情感音乐提升听众心理健康水平的效能。