## 4 Experiments
### Baseline CNN Structure  
To verify the performance of the proposed method, we compared the proposed method with a baseline model. In the DCASE 2017 challenge, several CNN-based models were proposed and showed good performance in weakly supervised AED [31; 32; 33]. We chose a CNN baseline model similar to the models proposed in the DCASE 2017 Challenge. The specific configuration of the baseline model is described in Table 2. The audio feature for the baseline was the same as that of the proposed model, a \(800\times 128\) normalized log mel spectrogram image. The baseline model consisted of four stacks of two convolution layers and a max pooling layer. The last max pooling layer was connected to two dense layers to produce segment-level predictions, and the segment-level predictions were aggregated in the global pooling layer.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Layers** & **Output Size** & **DSNet** & **DSNet-RNN** \\ \hline Convolution & \(800\times 128\times 32\) & [\(3\times 3\), \(32\) conv] \\ \hline Dense block & \(800\times 128\times 32\) & \begin{tabular}{c} [\(3\times 3\), \(16\) conv] \(\times\) \(4\) \\ [\(1\times 1\), \(32\) conv] \\ \end{tabular} \\ \hline SE block & \(800\times 128\times 32\) & bottleneck size \(8\) \\ \hline Max-pooling & \(800\times 64\times 32\) & \(1\times 2\) max pool \\ \hline Dense block & \(800\times 64\times 48\) & \begin{tabular}{c} [\(3\times 3\), \(16\) conv] \(\times\) \(4\) \\ [\(1\times 1\), \(48\) conv] \\ \end{tabular} \\ \hline SE block & \(800\times 64\times 48\) & bottleneck size \(12\) \\ \hline Max-pooling & \(400\times 32\times 48\) & \begin{tabular}{c} [\(3\times 3\), \(16\) conv] \(\times\) \(4\) \\ [\(1\times 1\), \(64\) conv] \\ \end{tabular} \\ \hline Dense block & \(400\times 32\times 64\) & \begin{tabular}{c} [\(3\times 3\), \(16\) conv] \(\times\) \(4\) \\ [\(1\times 1\), \(64\) conv] \\ \end{tabular} \\ \hline SE block & \(400\times 32\times 64\) & bottleneck size \(16\) \\ \hline Max-pooling & \(200\times 16\times 64\) & \begin{tabular}{c} 2 \(\times\) \(2\) max pool \\ [\(1\times 1\), \(64\) conv] \\ \end{tabular} \\ \hline SE block & \(200\times 16\times 64\) & bottleneck size \(16\) \\ \hline Max-pooling & \(100\times 8\times 64\) & \begin{tabular}{c} 2 \(\times\) \(2\) max pool \\ [\(1\times 1\), \(64\) conv] \\ \end{tabular} \\ \hline SE block & \(200\times 16\times 64\) & bottleneck size \(16\) \\ \hline Max-pooling & \(100\times 8\times 64\) & \begin{tabular}{c} 2 \(\times\) \(2\) max pool \\ [\(1\times 1\), \(64\) conv] \\ \end{tabular} \\ \hline SE block & \(200\times 16\times 64\) & bottleneck size \(16\) \\ \hline Max-pooling & \(100\times 8\times 64\) & \begin{tabular}{c} 2 \(\times\) \(2\) max pool \\ [\(1\times 1\), \(64\) conv] \\ \end{tabular} \\ \hline Reshape & \(100\times 8\times 64\) & \begin{tabular}{c} 2 \(\times\) \(2\) max pool \\ [\(1\) \\ \end{tabular} \\ \hline Segment-level prediction & \(100\times 17\) & \
\begin{tabular}{c} 256 dense(ReLU) \\ [\(17\) dense(sigmoid) \\ \end{tabular} &
\begin{tabular}{c} 128 Bi-GRUs \\ [\(17\) dense(sigmoid) \\ \end{tabular} \\ \hline Clip-level prediction & \(17\) & global LSE pooling \\ \hline Parameters & - & 0.32 M & 0.69 M \\ \hline \hline \end{tabular}
\end{table}
Table 1: DSNet and DSNet-RNN architectures.