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

2025-05-27

AI Summary

Non-expert music producers lack accessible tools for directly translating natural language descriptions into audio effect (Fx) parameters—such as EQ gain/frequency or reverb decay—without requiring domain expertise, model fine-tuning, or task-specific training. Method: We propose Text2Fx, a zero-shot mapping paradigm leveraging large language models (LLMs) conditioned on three complementary context types: (i) audio DSP features, (ii) executable DSP function code, and (iii) few-shot parameter examples—jointly enhancing semantic-to-numerical alignment in the parameter space. Contribution/Results: Text2Fx achieves state-of-the-art performance in zero-shot EQ and reverb parameter prediction, outperforming conventional optimization-based methods. It offers strong interpretability—generated parameters are grounded in DSP principles—and immediate usability via text-driven, plug-and-play audio control. To our knowledge, this is the first framework enabling general-purpose, zero-shot, and physically interpretable audio effect parameter generation.

Technology Category

• Natural Language Processing: Generation
• Machine Learning: Mixture of Experts (MoE)
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

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.

Problem

• Predict audio effects parameters from natural language descriptions
• Map text to Fx parameters for equalization and reverberation
• Enable zero-shot Fx parameter generation without task-specific training

Innovation

• LLMs predict audio effects from text
• Zero-shot mapping of text to Fx parameters
• In-context examples enhance DSP performance