## 4 Conclusions  
In this paper, we presented a model using DCMA and soft parameter sharing to simultaneously detect and localize sound events. The CNN-based encoder with parameter sharing exchanges intermediate features in the CNN layers for the SED and DOAE, and the DCMA-based decoder efficiently outputs three predictions to detect overlapping events in the DCASE2021 Task3. In particular, key and value vectors for either SED or DOAE stream in the DCMA were given from the other stream to efficiently learn the association between SED and DOAE features. Acoustic features, such as additional spectral information and chroma features, that had not been commonly used in the SELD task were additionally adopted to improve the performance. Experimental results demonstrated that our efficient model using one common decoder block based on the DCMA was effective for the SELD task with up to three overlapping events while retaining a high \(LR_{\text{CD}}\) with the track-wise output format. As a future work, we will study prediction rules to induce non-independent and reliable results between the FC blocks. For further improvement, we plan to develop an ensemble network including the presented model. \begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**System** & **Feature** & **Format** & \(ER_{\leq_{20^{\circ}}}\) & \(F_{\leq_{20^{\circ}}}\) **(\%)** & \(LE_{\text{CD}}\) **(\%)** & \(LR_{\text{CD}}\) **(\%)** & \(D_{\text{SELD}}\) & **Rank Sum** \\ \hline Baseline & mel sp.+IV & FOA & 0.73 & 30.7 & 24.5 & 44.8 & 0.528 & 28 \\ (\#1) [PERSON] et al. [15] & magnitude sp. + PCEN sp. + IPD & FOA & 0.43 & 69.9 & **11.1** & 73.2 & 0.265 & 10 \\ (\#2) [PERSON] et al. [17] & eigenvector-aug. log-sp. + mel sp. & FOA & **0.37** & **73.7** & 11.2 & 74.1 & **0.239** & 7 \\ (\#3) [PERSON] et al. [41] & mel sp.+IV & FOA & 0.65 & 57.1 & 17.5 & 62.8 & 0.387 & 24 \\ (\#5) [PERSON] et al. [19] & mel sp.+IV & Both & 0.44 & 69.6 & 13.7 & 74.2 & 0.270 & 13 \\ (\#6) [PERSON] et al. [42] & mel sp.+IV + GCC-PHAT & Both & 0.46 & 63.2 & 13.9 & 62.9 & 0.319 & 20 \\ DCMA-SELD & mel sp.+add. sp.+chroma+IV & FOA & \(0.41\pm 0.02\) & \(67.1\pm 2.1\) & \(13.6\pm 0.7\) & \(\textbf{79.0}\pm 2.0\) & \(0.256\pm 0.016\) & 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance of our DCMA-SELD and the models submitted to the DCASE2021 challenge on the TNSSE 2021 development dataset. The baseline system in the challenge corresponds to the modified version of the SELDnet in [40]. The numbers after # denote the rank of the model in the challenge. “Rank sum” represents the sum of the rankings of the four metrics used as the challenge ranking evaluation method. The highest performance in each metric is indicated in bold. **Author Contributions:** Conceptualization, S.-H.L., [PERSON]-W.H., [PERSON]-H.S. and H.-M.P. ; methodology, S.-H.L., [PERSON]-W.H., [PERSON]-H.S. and H.-M.P. ; software, S.-H.L., [PERSON]-W.H. and H.-M.P. ; validation, S.-H.L. and [PERSON]-W.H. ; formal analysis, S.-H.L. and [PERSON]-W.H. ; investigation, S.-H.L., [PERSON]-W.H., [PERSON]-H.S. and H.-M.P. ; resources, [PERSON]-H.S. and H.-M.P. ; data curation, [PERSON]-[PERSON] and [PERSON]; writing--original draft preparation, [PERSON]-[PERSON], [PERSON], [PERSON] and [PERSON]-[PERSON]; writing--review and editing, [PERSON]-[PERSON], [PERSON], [PERSON] and [PERSON]; visualization, [PERSON]-[PERSON]; supervision, [PERSON]-[PERSON]; project administration, [PERSON]-M.P. ; funding acquisition, [PERSON] and [PERSON]-[PERSON]. All authors have read and agreed to the published version of the manuscript. **Funding:** This work was supported by the Institute of Information and communications Technology Planning and evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2020-0-00860, Development of acoustic-based micro sensor device for disaster and safety with multi-role support and disaster situation recognition technology and No. 2019-0-01376, Development of the multi-speaker conversational speech recognition technology).