# Weakly Supervised U-Net with Limited Upsampling for Sound Event Detection
## 4 Experiments
### Summary of Experimental Results  
As shown in Tables 9-12, the proposed methods improved the audio tagging and sound event detection performances in most of the cases. Comparing U-Net and the proposed LUU-Net with the same GWRP, LUU-Net improved the tagging F1 score by up to 1.7% and the detection score by up to 1.5%. The training became more than three-times faster. In summary, the proposed LUU-Net slightly improved the tagging and detection performances with much faster model learning. Looking into the detailed precision and \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Pooling**} & \multicolumn{3}{c}{**AT Task**} & \multicolumn{3}{c}{**SED Task**} & \multirow{2}{*}{**\#Step/s**} \\  & & **Method** & **mPrc** & **mRcl** & & & **mPrc** & **mRcl** & **mF1** & **mF1** & **mF1** \\ \hline \multirow{3}{*}{CNN} & GWRP & 32.2 & 57.4 & 38.7 & 32.4 & 27.8 & 27.9 & 3.59 \\ \cline{2-9}  & \(\text{G}\text{T}\text{A}_{\text{a}=0.2}\) & 27.4 & 62.3 & 36.7 & 59.8 & 9.0 & 14.8 & \\ \cline{2-9}  & \(\text{G}\text{T}\text{A}_{\text{a}=0}\) & 26.6 & 62.9 & 36.1 & 62.6 & 10.4 & 16.7 & 4.42 \\ \cline{2-9}  & \(\text{G}\text{T}\text{A}_{\text{a}=-0.4}\) & 41.6 & 33.8 & 32.3 & 30.0 & 28.3 & 24.9 & \\ \hline \multirow{3}{*}{U-Net} & GWRP & 36.0 & 62.3 & 44.2 & 33.8 & 37.3 & 34.1 & 8.64 \\ \cline{2-9}  & \(\text{G}\text{T}\text{A}_{\text{a}=0}\) & 34.4 & 61.1 & 42.9 & 54.2 & 25.8 & 33.5 & \\ \cline{2-9}  & \(\text{G}\text{T}\text{A}_{\text{a}=-0}\) & 33.5 & 62.0 & 42.5 & 50.9 & 28.2 & 34.8 & 12.51 \\ \cline{2-9}  & \(\text{G}\text{T}\text{A}_{\text{a}=-0.4}\) & 50.6 & 50.0 & 48.8 & 33.8 & 40.1 & 34.8 & \\ \hline \multirow{3}{*}{LUU-Net} & AlphaMEX & 39.0 & 57.9 & 45.4 & 51.4 & 15.8 & 22.7 & 21.05 \\ \cline{2-9}  & MEX & 36.8 & 60.7 & 44.3 & 37.8 & 32.4 & 33.1 & 32.35 \\ \cline{2-9}  & GWRP & 38.5 & 60.1 & 45.5 & 37.8 & 33.8 & 34.3 & 28.21 \\ \cline{2-9}  & \(\text{G}\text{T}\text{A}_{\text{a}=0.2}\) & 37.7 & 60.4 & 45.4 & 50.4 & 27.3 & 34.1 & \\ \cline{2-9}  & \(\text{G}\text{T}\text{A}_{\text{a}=0}\) & 38.7 & 60.9 & 46.0 & 47.4 & 29.9 & 35.3 & 34.92 \\ \cline{2-9}  & \(\text{G}\text{T}\text{A}_{\text{a}=-0.4}\) & 47.3 & 53.4 & 48.8 & 34.4 & 38.4 & 34.5 & \\ \hline \hline \end{tabular}
\end{table} 
Table 12: AT and SED results on the dataset generated by the mixed policy. CNN, U-Net, and the proposed LUU-Net are shown in Tables 4–6, respectively. Pooling methods AlphaMEX, MEX, GWRP, and the proposed GTAP with varying \(\alpha\) values. Figure 11: 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 mixed synthetic policy. (**b**) Ground truth labels. (**c–h**) display sound event labels predicted by (**c**) CNN with GWRP, (**d**) U-Net with GWRP, LUU-Net with (**e**) GWRP, (**f**) AlphaMEX, (**g**) MEX, and (**h**) GTAP\({}_{\text{a}=-0.4}\). The \(x\)-axis is the time in seconds, and the \(y\)-axis represents the frequency (**a**) or event labels (**b–h**). recall scores, an interesting property was observed. In all four synthetic cases (original, longer clipping, random onset, and mixed), U-Net with GWRP usually showed relatively low precision scores and high recall scores when compared to those of LUU-Net with the same global pooling. Because the sound event onset timings were the same for all the frequency units, the segmentation targets should be varied in time only. The limited upsampling in the LUU-Net decoder provided blocked averaging in the frequency axis, resulting in low frequency resolution. A lower resolution is less likely to be affected by outliers, and LUU-Net did not change the time resolution, so there was no difference in the detection targets. This explains the high precision scores of LUU-Net. On the contrary, lower resolution is less effective in the precise exclusion of false units, so the recall scores of LUU-Net were lower than U-Net.