Sound Event Detection (SED) is challenging in noisy environments where overlapping sounds obscure target events. Language-queried audio source separation (LASS) aims to isolate the target sound events from a noisy clip. However, this approach can fail when the exact target sound is unknown, particularly in noisy test sets, leading to reduced performance. To address this issue, we leverage the capabilities of large language models (LLMs) to analyze and summarize acoustic data. By using LLMs to identify and select specific noise types, we implement a noise augmentation method for noise-robust fine-tuning. The fine-tuned model is applied to predict clip-wise event predictions as text queries for the LASS model. Our studies demonstrate that the proposed method improves SED performance in noisy environments. This work represents an early application of LLMs in noise-robust SED and suggests a promising direction for handling overlapping events in SED. Codes and pretrained models are available at https://github.com/apple-yinhan/Noise-robust-SED.

翻译：声音事件检测在噪声环境中面临挑战，因为重叠的声音会掩盖目标事件。语言查询音频源分离技术旨在从含噪音频片段中分离出目标声音事件。然而，当确切的目标声音未知时，特别是在含噪测试集中，该方法可能失效，导致性能下降。为解决这一问题，我们利用大语言模型的能力来分析和总结声学数据。通过使用大语言模型识别并选择特定噪声类型，我们实现了一种用于噪声鲁棒性微调的噪声增强方法。微调后的模型被用于预测片段级别的事件，并作为文本查询输入至LASS模型。我们的研究表明，所提出的方法提升了噪声环境下的声音事件检测性能。这项工作代表了大语言模型在噪声鲁棒性声音事件检测中的早期应用，并为处理声音事件检测中的重叠事件指明了一个有前景的方向。代码与预训练模型可在 https://github.com/apple-yinhan/Noise-robust-SED 获取。