Improving Weakly Supervised Sound Event Detection with Causal Intervention

• URL: http://arxiv.org/abs/2303.05678v1
• Date: Fri, 10 Mar 2023 03:13:36 GMT
• Title: Improving Weakly Supervised Sound Event Detection with Causal Intervention
• Authors: Yifei Xin, Dongchao Yang, Fan Cui, Yujun Wang, Yuexian Zou
• Abstract summary: Existing weakly supervised sound event detection work has not explored both types of co-occurrences simultaneously. We first establish a structural causal model (SCM) to reveal that the context is the main cause of co-occurrence confounders. Based on the causal analysis, we propose a causal intervention (CI) method for WSSED to remove the negative impact of co-occurrence confounders.
• Score: 46.229038054764956
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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