Performance Improvement of Language-Queried Audio Source Separation Based on Caption Augmentation From Large Language Models for DCASE Challenge 2024 Task 9

Understand

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.