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A Hybrid System of Sound Event Detection Transformer and Frame-wise Model for DCASE 2022 Task 4

arXiv 2210.09529 ↗First posted 18 Oct 2022cs.SD cs.LG eess.AScited by 0

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Abstract (arXiv)

In this paper, we describe in detail our system for DCASE 2022 Task4. The system combines two considerably different models: an end-to-end Sound Event Detection Transformer (SEDT) and a frame-wise model, Metric Learning and Focal Loss CNN (MLFL-CNN). The former is an event-wise model which learns event-level representations and predicts sound event categories and boundaries directly, while the latter is based on the widely adopted frame-classification scheme, under which each frame is classified into event categories and event boundaries are obtained by post-processing such as thresholding and smoothing. For SEDT, self-supervised pre-training using unlabeled data is applied, and semi-supervised learning is adopted by using an online teacher, which is updated from the student model using the Exponential Moving Average (EMA) strategy and generates reliable pseudo labels for weakly-labeled and unlabeled data. For the frame-wise model, the ICT-TOSHIBA system of DCASE 2021 Task 4 is used. Experimental results show that the hybrid system considerably outperforms either individual model and achieves psds1 of 0.420 and psds2 of 0.783 on the validation set without external data. The code is available at https://github.com/965694547/Hybrid-system-of-frame-wise-model-and-SEDT.

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References · 11

  1. 2017mixup: Beyond Empirical Risk Minimizationcited by 5,155
  2. 2020FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidencecited by 2,080
  3. 2016UnitBox: An Advanced Object Detection Networkcited by 205
  4. 2019A Framework for the Robust Evaluation of Sound Event Detectioncited by 28
  5. 2019Guided Learning Convolution System for DCASE 2019 Task 4cited by 8
  6. 2021Forward-Backward Convolutional Recurrent Neural Networks and Tag-Conditioned Convolutional Neural Networks for Weakly Labeled Semi-supervised Sound Event Detectioncited by 8
  7. 2021Sound Event Detection Transformer: An Event-based End-to-End Model for Sound Event Detectioncited by 7
  8. 2019Specialized Decision Surface and Disentangled Feature for Weakly-Supervised Polyphonic Sound Event Detectioncited by 5
  9. 2020Learning generic feature representation with synthetic data for weakly-supervised sound event detection by inter-frame distance losscited by 2
  10. 2021Couple Learning for semi-supervised sound event detectioncited by 1
  11. 2021SP-SEDT: Self-supervised Pre-training for Sound Event Detection Transformercited by 1

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