We present a prompt-engineering-based text-augmentation approach applied to a language-queried audio source separation (LASS) task. To enhance the performance of LASS, the proposed approach utilizes large language models (LLMs) to generate multiple captions corresponding to each sentence of the training dataset. To this end, we first perform experiments to identify the most effective prompts for caption augmentation with a smaller number of captions. A LASS model trained with these augmented captions demonstrates improved performance on the DCASE 2024 Task 9 validation set compared to that trained without augmentation. This study highlights the effectiveness of LLM-based caption augmentation in advancing language-queried audio source separation.

翻译：本文提出一种基于提示工程的文本增强方法，应用于语言查询音频源分离任务。为提升LASS性能，该方法利用大型语言模型为训练数据集的每个语句生成多条对应描述。为此，我们首先通过实验确定在较少描述数量下最有效的提示策略。使用增强描述训练的LASS模型在DCASE 2024任务9验证集上表现出优于未增强训练的性能。本研究揭示了基于LLM的描述增强在推进语言查询音频源分离技术中的有效性。