# Weakly Supervised U-Net with Limited Upsampling for Sound Event Detection
## 6 Conclusions  
In this paper, we proposed two methods to improve the performance of weakly supervised sound event detection. The first method was a modification of the conventional U-Net to perform limited upscaling (LUU-Net). Assuming that the upscaling function along the frequency axis of the U-Net is not necessary in sound event detection, upsampling gradually to the original size was applied to the time domain only. The second method was global threshold average pooling (GTAP), which replaced the conventional global  
Figure 15: Illustrations of audio tagging and sound event detection performances by varying audio mixing conditions. The subfigures on the left are graphical charts of average precision scores of GTAP with \(\alpha\in\{0.2,0.0,-0.4\}\). The x-axis is the audio synthetic policies. The upper chart is audio tagging performances, and the lower one is sound event detection performances. The subfigures at the center are graphical charts of the average recall scores, and those on the right are the average F1 scores. weighted rank pooling (GWRP). GWRP showed higher detection performance compared to global average pooling (GAP) and global max pooling (GMP) [16]. However, GWRP requires output map sorting in every pooling step. The proposed GTAP eliminates the sorting operation and replaces it with simple thresholding. To find the threshold, the mean and the standard deviation of the output feature map are necessary, which are much more computationally efficient than sorting. GTAP performs the global pooling by calculating the average of only the value above a certain threshold by using the characteristic that the rank-based weight of GWRP almost ignores smaller values. There was a significant improvement in training speed, with small or huge performance improvements depending on the mixing conditions. According to the experimental results, the proposed LUU-Net with GTAP greatly outperformed the CNN and U-Net in various mixing datasets. The advantage of applying the proposed LUU-Net is the improved computation time. The amount of model parameters was about 40% of the conventional U-Net, and the measured training time was 3-times faster than U-Net and 8-times faster than the baseline CNN. With the proposed GTAP, an additional 1.2-times faster speed was obtained. In terms of audio tagging and sound event detection performance, the proposed LUU-Net outperformed the U-Net and the CNN in almost all cases. Another advantage of the proposed GTAP is that, by varying a single hyperparameter, it can be adapted to various target applications with different requirements. As a conclusion, the major contribution of the proposed LUU-Net and GTAP is the reduction of the computation time without any performance loss. Future work includes the automatic adaptation of the hyperparameter \(\alpha\) for real applications. Conceptualization, S.L., H.K. and G.-J.J.; methodology, S.L. ; software, S.L. ; validation, S.L. and G.-J.J.; formal analysis, S.L. and H.K. ; investigation, H.K. ; resources, G.-J.J.; data curation, S.L. ; writing-original draft preparation, S.L. ; writing-review and editing, H.K. and G.-J.J.; visualization, S.L. ; supervision, G.-J.J.; project administration, G.-J.J.; funding acquisition, G.-J.J. All authors have read and agreed to the published version of the manuscript. This research was supported by the Culture, Sports and Tourism R&D Program through the Korea Creative Content Agency grant funded by the Ministry of Culture, Sports and Tourism in 2022 (project name: Development of high-speed music search technology using deep learning; project number: CR202104004). Not applicable. The original audio data are available at [[https://dcase.community/challenge2018](https://dcase.community/challenge2018)]([https://dcase.community/challenge2018](https://dcase.community/challenge2018)) (accessed on 18 February 2023).