Tidal MerzA: Combining affective modelling and autonomous code generation through Reinforcement Learning

TL;DR

Tidal-MerzA is a system that combines affective modelling and reinforcement learning to generate and adapt musical patterns in live coding performances, enhancing human-machine collaboration in creative music composition.

Contribution

It introduces a novel integration of affective modelling with reinforcement learning for live coding music, combining two models to improve adaptability and creative expression.

Findings

Enhanced adaptability of musical patterns to affective states

Improved syntactical correctness in generated music

Facilitated human-machine creative interaction

Abstract

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.