# Learning generic feature representation with synthetic data for weakly-supervised sound event detection by inter-frame distance loss

arXiv: https://arxiv.org/abs/2011.00695 · HTML: https://mlsanity.com/paper/2011.00695/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Nov 2020.

## Built on by (most cited first, 2 of 2)

- [Sound Event Detection Transformer: An Event-based End-to-End Model for Sound Event Detection](https://mlsanity.com/paper/2110.02011/) (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)

## Abstract (arXiv)

Due to the limitation of strong-labeled sound event detection data set, using synthetic data to improve the sound event detection system performance has been a new research focus. In this paper, we try to exploit the usage of synthetic data to improve the feature representation. Based on metric learning, we proposed inter-frame distance loss function for domain adaptation, and prove the effectiveness of it on sound event detection. We also applied multi-task learning with synthetic data. We find the the best performance can be achieved when the two methods being used together. The experiment on DCASE 2018 task 4 test set and DCASE 2019 task 4 synthetic set both show competitive results.