## 3 Experiments and Results
### Experimental Results and Discussions
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Decoder Architecture** & **Number of Parameters** & \(ER_{\leq_{\text{sup}}}\) & \(F_{\leq_{\text{sup}}}\) **(\%)** & \(LE_{\text{CD}}\) **(\%)** & \(LR_{\text{CD}}\) **(\%)** & \(D_{\text{SELD}}\) \\ \hline LSTM (3) & 34,617,325 & 0.49 \(\pm\) 0.02 & 59.2 \(\pm\) 2.1 & \(17.5\pm 0.8\) & \(76.2\pm 2.2\) & 0.309 \(\pm\) 0.016 \\ Multi-head self-attention (3) & 34,635,757 & 0.42 \(\pm\) 0.02 & 66.7 \(\pm\) 2.1 & \(\textbf{13.1}\pm 0.7\) & \(78.0\pm 2.1\) & 0.260 \(\pm\) 0.016 \\ Multi-head self-attention (1) & 17,813,485 & 0.45 \(\pm\) 0.02 & 63.0 \(\pm\) 2.2 & \(15.8\pm 0.9\) & \(77.5\pm 2.2\) & 0.283 \(\pm\) 0.017 \\ DCMA-SELD (1) & 26,218,477 & **0.41**\(\pm\) 0.02 & \(\textbf{67.1}\pm 2.1\) & \(13.6\pm 0.7\) & \(\textbf{79.0}\pm 2.0\) & \(\textbf{0.256}\pm 0.016\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance for different decoder architectures in our DCMA-SELD on the TNSSE 2021 development dataset. The numbers in the parentheses indicate the numbers of decoder blocks used in the models to perform three predictions. All data represent mean values and associated 95% confidence intervals. The highest performance in each metric is indicated in bold. sharing methods based on our model in Table 4 still showed scores higher than 75% in \(LR_{\text{CD}}\), which was better than the other participants. It is analyzed that the primary reason why our model can achieve overwhelming results in \(LR_{\text{CD}}\) compared to the others is that we applied the track-wise output format instead of the class-wise output format adopted in the others (the system in [15] is affected by class-wise predictions because it uses both the output formats together). The class-wise output format can only predict up to one event per class at a time. That is, if two or three events of the same class occur at the same time, one and two sound events, respectively, must be abandoned. However, since the track-wise output format predicts one sound event and direction per track, it is possible to cope even with the case where two or more events belonging to the same class occur at the same time. In the TNSSE 2021 dataset, the case where multiple events belonging to the same class occurs simultaneously reaches 10.45% [17], and the maximum possible score of \(LR_{\text{CD}}\) with ideal SED condition is 92.5% for the system adopting the class-wise output format [19]. In the DCASE2021 challenge, all the highly ranked systems compared here used the class-wise output format despite the handicap to prevent the possibility of mispredicting non-existent overlapped events that could cause performance degradation in other metrics. Above all, in spite of the possibility of the misprediction, our model ranked second in terms of both \(D_{\text{SELD}}\) and the challenge ranking evaluation method determined by the sum of the rankings of the four metrics while retaining a high \(LR_{\text{CD}}\) with the track-wise output format that could cope even with overlapped sound events. Therefore, combining our model with another model adopting the class-wise output format that can compensate for the possible misprediction of ours can be a promising ensemble method.