# Couple Learning for semi-supervised sound event detection

arXiv: https://arxiv.org/abs/2110.05809 · HTML: https://mlsanity.com/paper/2110.05809/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 25 references that are in the map.
- First posted on arXiv 12 Oct 2021.

## Builds on (most cited first, 9 of 9)

- [Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results](https://mlsanity.com/paper/1703.01780/) (2017)
- [Sound Event Detection and Separation: a Benchmark on Desed Synthetic Soundscapes](https://mlsanity.com/paper/2011.00801/) (2020)
- [Guided Learning Convolution System for DCASE 2019 Task 4](https://mlsanity.com/paper/1909.06178/) (2019)
- [Forward-Backward Convolutional Recurrent Neural Networks and Tag-Conditioned Convolutional Neural Networks for Weakly Labeled Semi-supervised Sound Event Detection](https://mlsanity.com/paper/2103.06581/) (2021)
- [Training Sound Event Detection On A Heterogeneous Dataset](https://mlsanity.com/paper/2007.03931/) (2020)
- [Pseudo Labeling and Negative Feedback Learning for Large-scale Multi-label Domain Classification](https://mlsanity.com/paper/2003.03728/) (2020)
- [Audiovisual transfer learning for audio tagging and sound event detection](https://mlsanity.com/paper/2106.05408/) (2021)
- [Intra-Utterance Similarity Preserving Knowledge Distillation for Audio Tagging](https://mlsanity.com/paper/2009.01759/) (2020)
- [Improving weakly supervised sound event detection with self-supervised auxiliary tasks](https://mlsanity.com/paper/2106.06858/) (2021)

## Built on by (most cited first, 1 of 1)

- [A Hybrid System of Sound Event Detection Transformer and Frame-wise Model for DCASE 2022 Task 4](https://mlsanity.com/paper/2210.09529/) (2022)

## Abstract (arXiv)

The recently proposed Mean Teacher method, which exploits large-scale unlabeled data in a self-ensembling manner, has achieved state-of-the-art results in several semi-supervised learning benchmarks. Spurred by current achievements, this paper proposes an effective Couple Learning method that combines a well-trained model and a Mean Teacher model. The suggested pseudo-labels generated model (PLG) increases strongly- and weakly-labeled data to improve the Mean Teacher method-s performance. Moreover, the Mean Teacher-s consistency cost reduces the noise impact in the pseudo-labels introduced by detection errors. The experimental results on Task 4 of the DCASE2020 challenge demonstrate the superiority of the proposed method, achieving about 44.25% F1-score on the public evaluation set, significantly outperforming the baseline system-s 32.39%. At the same time, we also propose a simple and effective experiment called the Variable Order Input (VOI) experiment, which proves the significance of the Couple Learning method. Our developed Couple Learning code is available on GitHub.