Weak Supervised Sound Event Detection Based on Puzzle CAM

The sound event detection method based on deep learning has achieved excellent performance. However, the training of high-performance sound event detection models depends on high-quality strong-label datasets, which brings great pressure and cost to the labeling work of datasets. In this paper, we propose a weakly supervised sound event detection method based on Puzzle Class Activation Map(CAM), which aims to obtain the timestamp information of sound events from a sound event classification network trained by weak labels. Specifically, the method discovers more complete regions of sound events by minimizing the difference between the CAM of the original features and the merged CAM of the separated feature blocks. CAM highlights the feature capture in the time dimension during the model decision-making process. We determine the occurrence time of the sound event based on the position of the frame where the feature is located, and use the model classification prediction to guide the CAM output to achieve weakly supervised sound event detection. Experiments on the Domestic Environment Sound Event Detection Dataset demonstrate that our proposed method exhibits superior performance compared to the baseline system in the task of sound event detection. Specifically, The CAM of a single system achieves the best PSDS2 value of 0.732. Furthermore, when utilizing the CAM ensemble of multiple systems, the PSDS2 value improves to 0.751. Both of these values are higher than those achieved by the baseline system.