{"ID":2834459,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.01559","arxiv_id":"2512.01559","title":"LLM2Fx-Tools: Tool Calling For Music Post-Production","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.","short_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...","url_abs":"https://arxiv.org/abs/2512.01559","url_pdf":"https://arxiv.org/pdf/2512.01559v2","authors":"[\"Seungheon Doh\",\"Junghyun Koo\",\"Marco A. Martínez-Ramírez\",\"Woosung Choi\",\"Wei-Hsiang Liao\",\"Qiyu Wu\",\"Juhan Nam\",\"Yuki Mitsufuji\"]","published":"2025-12-01T11:30:21Z","proceeding":"cs.SD","tasks":"[\"cs.SD\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}