# Improving Weakly Supervised Sound Event Detection with Causal Intervention

arXiv: https://arxiv.org/abs/2303.05678 · HTML: https://mlsanity.com/paper/2303.05678/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 20 references that are in the map.
- First posted on arXiv 10 Mar 2023.

## Builds on (most cited first, 11 of 11)

- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect](https://mlsanity.com/paper/2009.12991/) (2020)
- [Causal Intervention for Weakly-Supervised Semantic Segmentation](https://mlsanity.com/paper/2009.12547/) (2020)
- [HTS-AT: A Hierarchical Token-Semantic Audio Transformer for Sound Classification and Detection](https://mlsanity.com/paper/2202.00874/) (2022)
- [Sound Event Detection of Weakly Labelled Data with CNN-Transformer and Automatic Threshold Optimization](https://mlsanity.com/paper/1912.04761/) (2019)
- [Improving Weakly-supervised Object Localization via Causal Intervention](https://mlsanity.com/paper/2104.10351/) (2021)
- [Towards duration robust weakly supervised sound event detection](https://mlsanity.com/paper/2101.07687/) (2021)
- [A Critical View of the Structural Causal Model](https://mlsanity.com/paper/2002.10007/) (2020)
- [Specialized Decision Surface and Disentangled Feature for Weakly-Supervised Polyphonic Sound Event Detection](https://mlsanity.com/paper/1905.10091/) (2019)
- [A Causally Formulated Hazard Ratio Estimation through Backdoor Adjustment on Structural Causal Model](https://mlsanity.com/paper/2006.12573/) (2020)
- [Sound Event Detection Using Graph Laplacian Regularization Based on Event Co-occurrence](https://mlsanity.com/paper/1902.00816/) (2019)

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

- [ML-LMCL: Mutual Learning and Large-Margin Contrastive Learning for Improving ASR Robustness in Spoken Language Understanding](https://mlsanity.com/paper/2311.11375/) (2023)
- [Learning Normal Patterns in Musical Loops](https://mlsanity.com/paper/2505.23784/) (2025)

## Abstract (arXiv)

Existing weakly supervised sound event detection (WSSED) work has not explored both types of co-occurrences simultaneously, i.e., some sound events often co-occur, and their occurrences are usually accompanied by specific background sounds, so they would be inevitably entangled, causing misclassification and biased localization results with only clip-level supervision. To tackle this issue, we first establish a structural causal model (SCM) to reveal that the context is the main cause of co-occurrence confounders that mislead the model to learn spurious correlations between frames and clip-level labels. Based on the causal analysis, we propose a causal intervention (CI) method for WSSED to remove the negative impact of co-occurrence confounders by iteratively accumulating every possible context of each class and then re-projecting the contexts to the frame-level features for making the event boundary clearer. Experiments show that our method effectively improves the performance on multiple datasets and can generalize to various baseline models.