Peer Collaborative Learning for Polyphonic Sound Event Detection
This work addresses the problem of detecting overlapping sound events in audio for applications like acoustic scene analysis, but it is incremental as it builds on existing semi-supervised learning methods.
The paper tackles polyphonic sound event detection by applying peer collaborative learning, which combines ensemble and student-teacher knowledge distillation, to train robust models with limited labeled data, achieving an F1-score improvement of about 10% over the baseline on DCASE 2019 Task 4 datasets.
This paper describes that semi-supervised learning called peer collaborative learning (PCL) can be applied to the polyphonic sound event detection (PSED) task, which is one of the tasks in the Detection and Classification of Acoustic Scenes and Events (DCASE) challenge. Many deep learning models have been studied to find out what kind of sound events occur where and for how long in a given audio clip. The characteristic of PCL used in this paper is the combination of ensemble-based knowledge distillation into sub-networks and student-teacher model-based knowledge distillation, which can train a robust PSED model from a small amount of strongly labeled data, weakly labeled data, and a large amount of unlabeled data. We evaluated the proposed PCL model using the DCASE 2019 Task 4 datasets and achieved an F1-score improvement of about 10% compared to the baseline model.