PCL-AED: progressive acoustic event detection based on contrastive learning

Acoustic event detection faces great challenges in recognizing event categories and locating event temporal boundaries due to the scarcity of high-quality labeled data and the limitation of label granularity. To conquer these constraints, a novel data augmentation method, LTSRR, and a two-stage method, PCL-AED, are proposed in this study. LTSRR is developed to generate training samples with rich spectral characteristics, which significantly expands the diversity of the training data. Subsequently, PCL-AED puts up a segment-level progressive event classification strategy for alleviating the impact of uneven sample distribution on audio tagging, and a temporal contrast learning mechanism is designed to enhance the sensitivity of the model to event boundaries for acoustic event detection. Experiments on DCASE2018 and DCASE2021 Task 4 datasets show that the LTSRR and PCL-AED proposed in this paper exhibit competitive performance, providing a new stroke to the semi-supervised acoustic event detection task.