## 3 Experiments and Results
### Experimental Results and Discussions
In addition, the decoder was also replaced with the multi-head self-attention, which corresponded to the model in [13]. Since the model in [13] used one multi-head self-attention block for each track, three blocks were used in the task. On the other hand, even with three tracks, our model obtained three parallel prediction results from an FC layer after passing through one common decoder. Therefore, instead of using one multi-head self-attention block per track, we also evaluated the model performing three predictions after one common multi-head self-attention block. The LSTM-based decoder showed the lowest performance in all metrics. When the multi-head self-attention blocks were used as the decoder, the case of using three blocks achieved higher performance in all metrics than the case of using one common block. However, except for \(LE_{\text{CD}}\), which provided a greater error by 0.5\({}^{\circ}\), the decoder used in our DCMA-SELD outperformed the other decoder architectures despite using one common decoder block. This demonstrates that building an efficient model through one common decoder block which can produce sufficiently reliable outputs is more effective in improving generalization performance than using independent decoder blocks with too many weights in this task that has to predict three tracks. Table 6 compares the performance of the models trained with and without data augmentation for model training described in Section 2.2. The model to which the data augmentation was applied improved the performance in all metrics, which showed that the data augmentation was very useful in the task. Table 7 compares the performance on the development dataset of our model and highly ranked systems in the challenge (note that the presented model in this paper is an improved version of our previous model that placed fourth in the challenge as mentioned in the introduction). In the experimental results, the baseline system in the challenge corresponding to the modified version of the SELDnet in [40] showed significantly inferior performance in all metrics than the other methods, while the others showed relatively comparable performance. Especially, our model achieved the best performance for the metric \(LR_{\text{CD}}\) by recording the value of 79.0%. Excluding our model, [PERSON] et al. [19] showed the highest score of 74.2% in \(LR_{\text{CD}}\) among all the participants of the challenge, which was about 5% lower than ours. Moreover, both no parameter-sharing and hard parameter \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Augmentation** & \(ER_{\leq_{\text{sup}}}\) & \(F_{\leq_{\text{sup}}}\) **(\%)** & \(LE_{\text{CD}}\) **(\%)** & \(LR_{\text{CD}}\) **(\%)** & \(D_{\text{SELD}}\) \\ \hline Without augmentation & \(0.50\pm 0.02\) & \(58.6\pm 1.9\) & \(17.9\pm 0.9\) & \(76.5\pm 1.8\) & \(0.312\pm 0.014\) \\ With augmentation & \(\textbf{0.41}\pm 0.02\) & \(\textbf{67.1}\pm 2.1\) & \(\textbf{13.6}\pm 0.7\) & \(\textbf{79.0}\pm 2.0\) & \(\textbf{0.256}\pm 0.016\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance of our DCMA-SELD models trained with and without data augmentation for model training on the TNSSE 2021 development dataset. All data represent mean values and associated 95% confidence intervals. The highest performance in each metric is indicated in bold.