LLM2Fx-Tools: Tool Calling For Music Post-Production
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arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866910004054851584 |
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| author | Doh, Seungheon Koo, Junghyun Martínez-Ramírez, Marco A. Choi, Woosung Liao, Wei-Hsiang Wu, Qiyu Nam, Juhan Mitsufuji, Yuki |
| author_facet | Doh, Seungheon Koo, Junghyun Martínez-Ramírez, Marco A. Choi, Woosung Liao, Wei-Hsiang Wu, Qiyu Nam, Juhan Mitsufuji, Yuki |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_01559 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LLM2Fx-Tools: Tool Calling For Music Post-Production Doh, Seungheon Koo, Junghyun Martínez-Ramírez, Marco A. Choi, Woosung Liao, Wei-Hsiang Wu, Qiyu Nam, Juhan Mitsufuji, Yuki Sound 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. |
| title | LLM2Fx-Tools: Tool Calling For Music Post-Production |
| topic | Sound |
| url | https://arxiv.org/abs/2512.01559 |