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.

翻译：声音事件检测（SED）常面临数据不足的问题。DCASE2023挑战任务4的最新基线系统利用大规模预训练自监督学习（SelfSL）模型来缓解这一限制，其中预训练模型有助于为SED生成更具判别力的特征。然而，在挑战基线系统及多数参赛提交方案中，预训练模型仅被用作冻结的特征提取器，针对预训练模型的微调方法鲜有研究。本研究探讨了用于SED的预训练模型微调方法。我们首先将新提出的SelfSL模型ATST-Frame引入SED系统。ATST-Frame专为学习音频信号的帧级表示而设计，并在多项下游任务中取得了最先进（SOTA）性能。随后我们提出了一种利用（域内）无标签和标签化SED数据对ATST-Frame进行微调的方法。实验表明，所提方法克服了大规模预训练网络微调时的过拟合问题，我们的SED系统在DCASE挑战任务4数据集上取得了0.587/0.812 PSDS1/PSDS2分数的新SOTA结果。