## 3 Experiments and Results
### Experimental Results and Discussions  
Since the metadata information of the evaluation dataset is not provided even after the challenge, we cannot check the performance on the evaluation dataset. Instead, we compared the performance of the models on the test set in the development dataset. For all our results in the following tables, we included the 95% confidence intervals for each metric, which were estimated using the jackknife procedure, with the partial estimates calculated in a leave-one-out manner, excluding, in turns, one audio file from the test set, as presented in [8; 39]. Table 3 summarizes an ablation study on input features for our DCMA-SELD. Retaining the log-mel spectrogram and IVs as basic features for SED and DOAE, performance was evaluated when some of the four types of input features were dropped. When all four kinds of features were applied, the best results were obtained in all the metrics except for \(LR_{\text{CD}}\), which was 0.1% lower than the best score. The performance was degraded without some of them, which demonstrates that all types of features contribute to performance improvement. This is because the log-mel spectrogram, additional spectral information, and chroma features are obtained from spectra with different frequency resolutions and thus contain information on different aspects of the input audio signal. In the following experiments, all four types of features were used. Table 4 compares the performance when using different parameter-sharing methods in the encoder of our DCMA-SELD 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. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Parameter Sharing** & \(ER_{\leq_{\text{BP}}}\) & \(F_{\leq_{\text{BP}}}\) **(\%)** & \(LE_{\text{CD}}\) **(\%)** & \(LR_{\text{CD}}\) **(\%)** & \(D_{\text{SELD}}\) \\ \hline No parameter sharing & 0.46 \(\pm\) 0.02 & 63.2 \(\pm\) 1.9 & 14.3 \(\pm\) 0.8 & 75.4 \(\pm\) 2.1 & 0.287 \(\pm\) 0.014 \\ Hard parameter sharing & 0.44 \(\pm\) 0.02 & 65.6 \(\pm\) 2.0 & **13.6** \(\pm\) 0.7 & 75.9 \(\pm\) 2.0 & 0.278 \(\pm\) 0.015 \\ Soft parameter sharing & **0.41**\(\pm\) 0.02 & **67.1**\(\pm\) 2.1 & **13.6**\(\pm\) 0.7 & **79.0**\(\pm\) 2.0 & **0.256**\(\pm\) 0.016 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance for different parameter-sharing methods in the encoder of our DCMA-SELD 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. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Feature** & \(ER_{\leq_{\text{BP}}}\) & \(F_{\leq_{\text{BP}}}\) **(\%)** & \(LE_{\text{CD}}\) **(\%)** & \(LR_{\text{CD}}\) **(\%)** & \(D_{\text{SELD}}\) \\ \hline mel sp.+IV & 0.44 \(\pm\) 0.02 & 64.8 \(\pm\) 1.9 & 14.4 \(\pm\) 0.8 & **79.1**\(\pm\) 1.9 & 0.269 \(\pm\) 0.014 \\ mel sp.+add. sp. info.+IV & 0.44 \(\pm\) 0.02 & 64.2 \(\pm\) 2.1 & 14.8 \(\pm\) 0.7 & 78.8 \(\pm\) 2.1 & 0.273 \(\pm\) 0.016 \\ mel sp.+chroma+IV & 0.42 \(\pm\) 0.02 & 65.2 \(\pm\) 2.1 & 14.0 \(\pm\) 0.7 & 78.5 \(\pm\) 1.9 & 0.267 \(\pm\) 0.015 \\ mel sp.+add. sp. info.+chroma+IV & **0.41**\(\pm\) 0.02 & **67.1**\(\pm\) 2.1 & **13.6**\(\pm\) 0.7 & 79.0 \(\pm\) 2.0 & **0.256**\(\pm\) 0.016 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Ablation study on input features for our DCMA-SELD 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. Table 5 shows the performance when the decoder of our model was replaced with different architectures. As a basic decoder to deal with audio data, two-layer LSTM networks were evaluated. In each stream, three LSTM blocks in parallel were used to predict SED or DOAE outputs in this task where up to three overlapping events may occur.