用大模型直接从文字生成音频效果参数，零样本即可实现。
Can Large Language Models Predict Audio Effects Parameters from Natural Language?
- 用大模型零样本映射自然语言到均衡器和混响参数。
- 结合音频处理特征与代码示例，性能超越传统优化方法。
- 适合音乐制作新手或想快速调参的创作者使用。
在音乐制作中，通过自然语言操控音频效果参数有望降低非专业人士的技术门槛。我们提出 LLM2Fx 框架，利用大语言模型（LLMs）直接从文本描述预测音频效果参数，无需任务特定训练或微调。该方法解决文本到效果参数预测（Text2Fx）问题，将自然语言描述映射到均衡器与混响的效果参数。我们证明了大模型可零样本生成符合音色语义的参数，揭示音乐制作中音色与效果之间的关系。为提升性能，引入三类上下文示例：音频数字信号处理（DSP）特征、DSP函数代码和少量示例。结果表明，基于大模型的效果参数生成优于以往优化方法，在将自然语言转化为合适参数设置方面表现竞争力。此外，大模型可作为文本驱动的音频制作接口，推动更直观、易用的音乐创作工具发展。
原文摘要 · Abstract (English)
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.
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