  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.
