# SEED: Sound Event Early Detection via Evidential Uncertainty

arXiv: https://arxiv.org/abs/2202.02441 · HTML: https://mlsanity.com/paper/2202.02441/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 21 references that are in the map.
- First posted on arXiv 5 Feb 2022.

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

- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Evidential Deep Learning to Quantify Classification Uncertainty](https://mlsanity.com/paper/1806.01768/) (2018)
- [Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference](https://mlsanity.com/paper/1506.02158/) (2015)
- [Uncertainty Aware Semi-Supervised Learning on Graph Data](https://mlsanity.com/paper/2010.12783/) (2020)
- [The Benefit Of Temporally-Strong Labels In Audio Event Classification](https://mlsanity.com/paper/2105.07031/) (2021)
- [Multidimensional Uncertainty-Aware Evidential Neural Networks](https://mlsanity.com/paper/2012.13676/) (2020)
- [Training Sound Event Detection On A Heterogeneous Dataset](https://mlsanity.com/paper/2007.03931/) (2020)
- [Boosting Cross-Lingual Transfer via Self-Learning with Uncertainty Estimation](https://mlsanity.com/paper/2109.00194/) (2021)
- [Enabling Early Audio Event Detection with Neural Networks](https://mlsanity.com/paper/1712.02116/) (2017)

## Built on by (most cited first, 4 of 4)

- [A Comprehensive Survey on Evidential Deep Learning and Its Applications](https://mlsanity.com/paper/2409.04720/) (2024)
- [A Survey on Uncertainty Reasoning and Quantification for Decision Making: Belief Theory Meets Deep Learning](https://mlsanity.com/paper/2206.05675/) (2022)
- [Towards Trustworthy Multi-label Sewer Defect Classification via Evidential Deep Learning](https://mlsanity.com/paper/2210.13782/) (2022)
- [Spectrogram features for audio and speech analysis](https://mlsanity.com/paper/2603.14917/) (2026)

## Abstract (arXiv)

Sound Event Early Detection (SEED) is an essential task in recognizing the acoustic environments and soundscapes. However, most of the existing methods focus on the offline sound event detection, which suffers from the over-confidence issue of early-stage event detection and usually yield unreliable results. To solve the problem, we propose a novel Polyphonic Evidential Neural Network (PENet) to model the evidential uncertainty of the class probability with Beta distribution. Specifically, we use a Beta distribution to model the distribution of class probabilities, and the evidential uncertainty enriches uncertainty representation with evidence information, which plays a central role in reliable prediction. To further improve the event detection performance, we design the backtrack inference method that utilizes both the forward and backward audio features of an ongoing event. Experiments on the DESED database show that the proposed method can simultaneously improve 13.0% and 3.8% in time delay and detection F1 score compared to the state-of-the-art methods.