# Can Large Language Models Predict Audio Effects Parameters from Natural Language?

arXiv: https://arxiv.org/abs/2505.20770 · HTML: https://mlsanity.com/paper/2505.20770/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 May 2025.

## Built on by (most cited first, 3 of 3)

- [One Prompt, Many Sounds: Modeling Listener Variability in LLM-Based Equalization](https://mlsanity.com/paper/2601.09448/) (2026)
- [TimberAgent: Gram-Guided Retrieval for Executable Music Effect Control](https://mlsanity.com/paper/2603.09332/) (2026)
- [Direct Preference Density Alignment for Conversational Audio Equalization](https://mlsanity.com/paper/2609.12607/) (2026)

## Abstract (arXiv)

In music production, manipulating audio effects (Fx) parameters through natural language has the potential to reduce technical barriers for non-experts. We present LLM2Fx, a framework leveraging Large Language Models (LLMs) to predict Fx parameters directly from textual descriptions without requiring task-specific training or fine-tuning. Our approach address the text-to-effect parameter prediction (Text2Fx) task by mapping natural language descriptions to the corresponding Fx parameters for equalization and reverberation. We demonstrate that LLMs can generate Fx parameters in a zero-shot manner that elucidates the relationship between timbre semantics and audio effects in music production. To enhance performance, we introduce three types of in-context examples: audio Digital Signal Processing (DSP) features, DSP function code, and few-shot examples. Our results demonstrate that LLM-based Fx parameter generation outperforms previous optimization approaches, offering competitive performance in translating natural language descriptions to appropriate Fx settings. Furthermore, LLMs can serve as text-driven interfaces for audio production, paving the way for more intuitive and accessible music production tools.