  Sound event detection (SED) often suffers from the data deficiency problem.
The recent baseline system in the DCASE2023 challenge task 4 leverages the
large pretrained self-supervised learning (SelfSL) models to mitigate such
restriction, where the pretrained models help to produce more discriminative
features for SED. However, the pretrained models are regarded as a frozen
feature extractor in the challenge baseline system and most of the challenge
submissions, and fine-tuning of the pretrained models has been rarely studied.
In this work, we study the fine-tuning method of the pretrained models for SED.
We first introduce ATST-Frame, our newly proposed SelfSL model, to the SED
system. ATST-Frame was especially designed for learning frame-level
representations of audio signals and obtained state-of-the-art (SOTA)
performances on a series of downstream tasks. We then propose a fine-tuning
method for ATST-Frame using both (in-domain) unlabelled and labelled SED data.
Our experiments show that, the proposed method overcomes the overfitting
problem when fine-tuning the large pretrained network, and our SED system
obtains new SOTA results of 0.587/0.812 PSDS1/PSDS2 scores on the DCASE
challenge task 4 dataset.
