Peer Collaborative Learning for Polyphonic Sound Event Detection

TL;DR

This paper introduces peer collaborative learning (PCL), a semi-supervised approach combining ensemble and student-teacher distillation methods, to improve polyphonic sound event detection with limited labeled data, demonstrating significant performance gains.

Contribution

The paper proposes a novel PCL framework that effectively leverages unlabeled and weakly labeled data for robust sound event detection, combining ensemble and student-teacher distillation techniques.

Findings

Achieved about 10% F1-score improvement over baseline.

Demonstrated effectiveness on DCASE 2019 Task 4 dataset.

Validated robustness with limited labeled data.

Abstract

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.