  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.
