Not that Groove: Zero-Shot Symbolic Music Editing

TL;DR

This paper introduces a zero-shot symbolic music editing method using large language models and a novel notation, enabling complex drum groove modifications based on natural language instructions.

Contribution

It presents a new text-based notation and a benchmark for zero-shot symbolic music editing, along with an automated evaluation framework for musical constraints.

Findings

Top model achieved 68% success rate on automated tests.

Automated tests correlate highly with professional musicians' judgments.

The approach enables scalable, data-efficient music editing with LLMs.

Abstract

While recent advancements in AI music generation have predominantly focused on direct audio synthesis, these systems suffer from inherent rigidity, limiting their utility for professional music producers who require granular, highly malleable creative control. Symbolic music (e.g., MIDI) resolves this constraint by providing editable note-level parameters, yet the natural progression to instruction-driven symbolic music editing remains critically under-explored due to a severe scarcity of paired instruction-MIDI datasets. In this paper, we bypass this data bottleneck by formalizing zero-shot symbolic music editing as a structured reasoning task. We introduce a novel text-based "drumroll" notation that translates musical mechanics into a spatial, syntax-driven grid, empowering off-the-shelf Large Language Models (LLMs) to logically deduce and apply complex edits to drum grooves using only zero-shot prompting. To rigorously evaluate this paradigm, we propose Not that Groove, a comprehensive benchmark comprising thousands of drum grooves paired with specific, descriptive, and stylistic natural language instructions. Crucially, to overcome the prohibitive cost and subjectivity of human musical evaluation, we introduce a scalable, domain-informed automated unit-testing framework that symbolically verifies whether an edited groove satisfies the core constraints of the user's request. Our extensive experiments across eight state-of-the-art LLMs demonstrate the high efficacy of this approach, with the top-performing model achieving a 68% success rate on our automated unit tests. Furthermore, listening tests confirm that our programmatic unit tests align highly with the subjective judgments of professional musicians, establishing a robust, data-efficient, and scalable foundation for the future of controllable AI music production.