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Peer Collaborative Learning for Polyphonic Sound Event Detection

arXiv 2110.03511 ↗First posted 7 Oct 2021eess.AS cs.LG cs.SDcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 12 of its 22 references that are in the map. First posted on arXiv 7 Oct 2021.

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References · 12

  1. 2015Distilling the Knowledge in a Neural Networkcited by 10,076
  2. 2017mixup: Beyond Empirical Risk Minimizationcited by 5,155
  3. 2019SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognitioncited by 1,355
  4. 2016Temporal Ensembling for Semi-Supervised Learningcited by 1,054
  5. 2020Peer Collaborative Learning for Online Knowledge Distillationcited by 58
  6. 2019Ensemble Knowledge Distillation for Learning Improved and Efficient Networkscited by 46
  7. 2019Feature Fusion for Online Mutual Knowledge Distillationcited by 40
  8. 2018Large-Scale Weakly Labeled Semi-Supervised Sound Event Detection in Domestic Environmentscited by 10
  9. 2019Guided Learning Convolution System for DCASE 2019 Task 4cited by 8
  10. 2021Forward-Backward Convolutional Recurrent Neural Networks and Tag-Conditioned Convolutional Neural Networks for Weakly Labeled Semi-supervised Sound Event Detectioncited by 8
  11. 2020Training Sound Event Detection On A Heterogeneous Datasetcited by 7
  12. 2019Weakly Labeled Sound Event Detection Using Tri-training and Adversarial Learningcited by 2

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