# Guided Learning Convolution System for DCASE 2019 Task 4

arXiv: https://arxiv.org/abs/1909.06178 · HTML: https://mlsanity.com/paper/1909.06178/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2019.

## Built on by (most cited first, 8 of 8)

- [Hear Me Out: Fusional Approaches for Audio Augmented Temporal Action Localization](https://mlsanity.com/paper/2106.14118/) (2021)
- [Sound Event Detection Transformer: An Event-based End-to-End Model for Sound Event Detection](https://mlsanity.com/paper/2110.02011/) (2021)
- [DiffSED: Sound Event Detection with Denoising Diffusion](https://mlsanity.com/paper/2308.07293/) (2023)
- [Couple Learning for semi-supervised sound event detection](https://mlsanity.com/paper/2110.05809/) (2021)
- [Peer Collaborative Learning for Polyphonic Sound Event Detection](https://mlsanity.com/paper/2110.03511/) (2021)
- [A Hybrid System of Sound Event Detection Transformer and Frame-wise Model for DCASE 2022 Task 4](https://mlsanity.com/paper/2210.09529/) (2022)
- [Improving snore detection under limited dataset through harmonic/percussive source separation and convolutional neural networks](https://mlsanity.com/paper/2410.23796/) (2024)
- [Spectrogram features for audio and speech analysis](https://mlsanity.com/paper/2603.14917/) (2026)

## Abstract (arXiv)

In this paper, we describe in detail the system we submitted to DCASE2019 task 4: sound event detection (SED) in domestic environments. We employ a convolutional neural network (CNN) with an embedding-level attention pooling module to solve it. By considering the interference caused by the co-occurrence of multiple events in the unbalanced dataset, we utilize the disentangled feature to raise the performance of the model. To take advantage of the unlabeled data, we adopt Guided Learning for semi-supervised learning. A group of median filters with adaptive window sizes is utilized in the post-processing of output probabilities of the model. We also analyze the effect of the synthetic data on the performance of the model and finally achieve an event-based F-measure of 45.43% on the validation set and an event-based F-measure of 42.7% on the test set. The system we submitted to the challenge achieves the best performance compared to those of other participates.