用强化学习让机器实时生成有情感的音乐代码，实现人机共演。
Tidal MerzA: Combining affective modelling and autonomous code generation through Reinforcement Learning
- 分两阶段：生成音乐代码片段，再通过强化学习匹配目标情绪
- 在TidalCycles框架中实现情感与语法双重优化，提升创作灵活性
- 适合音乐生成、人机协作与艺术计算研究者
本文提出Tidal-MerzA，一种用于现场编程中人机协作表演的新型系统，聚焦于音乐模式的生成。该系统融合两个基础模型：ALCAA（情感式现场编程自主代理）与Tidal Fuzz计算框架。通过将情感建模与计算生成结合，利用强化学习技术动态调整TidalCycles框架中的音乐组合参数，确保生成模式兼具情感特质与语法正确性。Tidal-MerzA引入两种独立智能体：一者负责生成用于音乐表达的微型符号串，另一者则通过强化学习使音乐与目标情感状态对齐。该方法提升了现场编程实践的适应性与创造性潜力，推动了人机协同创作的探索。Tidal-MerzA推进了计算音乐生成领域的发展，为人工智能融入艺术实践提供了新范式。
原文摘要 · Abstract (English)
This paper presents Tidal-MerzA, a novel system designed for collaborative performances between humans and a machine agent in the context of live coding, specifically focusing on the generation of musical patterns. Tidal-MerzA fuses two foundational models: ALCAA (Affective Live Coding Autonomous Agent) and Tidal Fuzz, a computational framework. By integrating affective modelling with computational generation, this system leverages reinforcement learning techniques to dynamically adapt music composition parameters within the TidalCycles framework, ensuring both affective qualities to the patterns and syntactical correctness. The development of Tidal-MerzA introduces two distinct agents: one focusing on the generation of mini-notation strings for musical expression, and another on the alignment of music with targeted affective states through reinforcement learning. This approach enhances the adaptability and creative potential of live coding practices and allows exploration of human-machine creative interactions. Tidal-MerzA advances the field of computational music generation, presenting a novel methodology for incorporating artificial intelligence into artistic practices.
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