# Weakly Supervised U-Net with Limited Upsampling for Sound Event Detection
## 4 Experiments
### Longer Clipping Synthetic Policy  
This section describes the experimental results on the dataset synthesized by the longer clipping policy given in Table 1. The mean and standard deviation of the event length included in this dataset were 3.14 and 1.67 s, respectively. The event length was limited to 5 s, so this dataset also included overlaps between events around 3 and 5.5 s. The audio tagging (AT) and sound event detection (SED) results are shown in Table 10. Similar to the original synthetic policy, U-Net showed much higher tagging and detection performances (63.7 and 47.4 F1 scores), as well as reduced computational overhead. Comparing U-Net and LUU-Net, with the same GWRP, LUU-Net showed 0.7 higher tagging (64.4) and 0.6 lower detection (46.8) F1 scores. With longer event lengths, the proposed LUU-Net was less advantageous, except in the computational efficiency. This is because the model parameters of U-Net were better trained with longer event lengths, so more stable performance than that of the original synthetic policy was obtained. The tagging F1 scores of GWRP, AlphaMEX, and MEX with LUU-Net were all similar, but AlphaMEX showed lower detection performance than the other pooling methods. For the CNN, U-Net, and LUU-Net, we applied the proposed GTAP with \(\alpha\in\{0.2,0,-0.4\}\) in terms of the mFi. There were huge performance degradations in the CNN with GTAP, similar to the original synthetic policy. Especially in SED, much higher precision was obtained, but recall dropped drastically. Because the proposed GTAP cuts off the output map, many activations were lost, and therefore, the recall metrics dropped greatly. With U-Net, little performance drops were observed with GTAP. With the proposed LUU-Net, GTAP improved both the AT and SED performances. The tagging F1 score with GWRP was 64.4 and with GTAP with \(\alpha\in\{0.2,0,-0.4\}\), 64.4, 64.1, and 64.7, respectively, so the best F1 score was obtained with \(\alpha=-0.4\), which is the same as the experimental results with the original synthetic policy. The detection F1 scores were 44.8, 46.6, and 48.0, respectively, and the best was also with \(\alpha=-0.4\). Interestingly, it was not as good as the other \(\alpha\) values in the original policy. However, \(\alpha=-0.4\) was best in both the tagging and detection tasks. This implies that longer event lengths should provide more obvious unit labels and improve both tagging and detection performances. Figure 8: Sound event detection examples with various models and global pooling methods. The vertical axis in (**b**–**h**) represents the class number. (**a**) Spectrogram of the input mixture sample generated by the original synthetic policy. The \(x\)-axis is the time in seconds (10 s long), and the \(y\)-axis is the frequency (only \(0\sim 4\)kHz are shown). (**b**) Ground truth labels. There are 3 distinctive sound events, represented by bright red lines. For (**b**–**h**), the \(x\)-axis is the time in seconds aligned with the \(x\)-axis of the spectrogram in (**a**), and the \(y\)-axis represents the event labels. (**c**–**h**) display sound event labels predicted by (**c**) CNN with GWRP, (**d**) U-Net with GWRP, (**e**) LUU-Net with GWRP, (**f**) LUU-Net with AlphaMEX, (**g**) LUU-Net with MEX, and (**h**) LUU-Net with \(\text{GTAP}_{\alpha=0}\). Figure 9 shows the sound event detection examples for the mixtures generated by the longer clipping policy. Various models and various global pooling methods were applied. The U-Net (d) and LUU-Net (e) results were better than those of CNN (c), but there were still prediction errors, as well as false detections. The proposed GTAP with \(\alpha=-0.4\) (h) showed the best prediction performance. Almost no false predictions were observed, and the overlap between the second and the third events was also detected.