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

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.

Semi-supervsied Learning-based Sound Event Detection using Freuqency Dynamic Convolution with Large Kernel Attention for DCASE Challenge 2023 Task 4

This report proposes a frequency dynamic convolution (FDY) with a large kernel attention (LKA)-convolutional recurrent neural network (CRNN) with a pre-trained bidirectional encoder representation from audio transformers (BEATs) embedding-based sound event detection (SED) model that employs a mean-teacher and pseudo-label approach to address the challenge of limited labeled data for DCASE 2023 Task 4. The proposed FDY with LKA integrates the FDY and LKA module to effectively capture time-frequency patterns, long-term dependencies, and high-level semantic information in audio signals. The proposed FDY with LKA-CRNN with a BEATs embedding network is initially trained on the entire DCASE 2023 Task 4 dataset using the mean-teacher approach, generating pseudo-labels for weakly labeled, unlabeled, and the AudioSet. Subsequently, the proposed SED model is retrained using the same pseudo-label approach. A subset of these models is selected for submission, demonstrating superior F1-scores and polyphonic SED score performance on the DCASE 2023 Challenge Task 4 validation dataset.