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arXiv · 2512.01559

LLM2Fx-Tools: Tool Calling For Music Post-Production

Seungheon Doh·Junghyun Koo·Marco A. Martínez-Ramírez·Woosung Choi·Wei-Hsiang Liao·Qiyu Wu·Juhan Nam·Yuki Mitsufuji
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Abstract

This paper introduces LLM2Fx-Tools, a multimodal tool-calling framework that generates executable sequences of audio effects (Fx-chain) for music post-production. LLM2Fx-Tools uses a large language model (LLM) to understand audio inputs, select audio effects types, determine their order, and estimate parameters, guided by chain-of-thought (CoT) planning. We also present LP-Fx, a new instruction-following dataset with structured CoT annotations and tool calls for audio effects modules. Experiments show that LLM2Fx-Tools can infer an Fx-chain and its parameters from pairs of unprocessed and processed audio, enabled by autoregressive sequence modeling, tool calling, and CoT reasoning. We further validate the system in a style transfer setting, where audio effects information is transferred from a reference source and applied to new content. Finally, LLM-as-a-judge evaluation demonstrates that our approach generates appropriate CoT reasoning and responses for music production queries. To our knowledge, this is the first work to apply LLM-based tool calling to audio effects modules, enabling interpretable and controllable music production.

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Seungheon Doh, Junghyun Koo, Marco A. Martínez-Ramírez, Woosung Choi, Wei-Hsiang Liao, Qiyu Wu, Juhan Nam, Yuki Mitsufuji. 2025-12-01. LLM2Fx-Tools: Tool Calling For Music Post-Production. https://arxiv.org/abs/2512.01559

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