## 5 Conclusions  
In this paper, we have researched to find the performance enhancement method for model-complexity-limited ASC tasks. We first paid attention to the model ensemble techniques as the method for performance enhancement. Ensembles of several models are a \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Log Loss**} & \multicolumn{2}{c}{**Macro-Averaged**} & \multirow{2}{*}{**Model Size**} \\  & & \multicolumn{1}{c}{**F\({}_{1}\)-Score**} & \\ \hline Vanilla & 1.035 & 65.44\% & 43.6 KB \\ Proposed Mean-Teacher with \(\beta=0.5\) & 1.027 & 66.15\% & 43.6 KB \\ Proposed Mean-Teacher with \(\beta=1\) & 1.012 & 67.05\% & 43.6 KB \\ Proposed Mean-Teacher with \(\beta=2.5\) & **1.004** & **67.49\%** & 43.6 KB \\ Proposed Mean-Teacher with \(\beta=5\) & 1.035 & 66.55\% & 43.6 KB \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Performances of the 1-bit-quantized WRN models. Figure 7: Schematic diagrams of (**a**) the ASC model with wide ResNet (WRN), (**b**) the residual block, and (**c**) the down-sampling block. The “\(2\times\) channels” layer in the down-sampling block is a layer for doubling the number of channels with zero paddings for the skip connection. well-known auxiliary method for performance enhancement, but they require a large model size. Therefore, the model ensemble technique is hard to use for the model-complexity-limited ASC problem. In order to tackle this problem, we modify the mean-teacher model, which is developed for consistency learning in the semi-supervised learning, and apply it to our problem. In order to evaluate our model, ASC experiments were performed with a database from DCASE 2021 Task 1A. First, our mean-teacher model was applied to a ResNet-based low-model-complexity ASC model to enhance the classification performance. As the evaluation result, our proposed method was able to improve the log loss performance up to 1.009 and F\({}_{1}\)-score performance by 67.12%, whereas the vanilla model showed a log loss performance of 1.052 and an F\({}_{1}\)-score performance of 65.79%. Moreover, our proposed model was more effective than the simple application of the temporal ensembling and mean-teacher model techniques, which are developed for the semi-supervised learning. Finally, our model was applied to another model, namely the 1-bit quantized WRN model. The model with the proposed technique was able to improve the log loss performance up to 1.004 and the F\({}_{1}\)-score performance by 67.49%, whereas the vanilla model showed a log loss of 1.035 and an F\({}_{1}\)-score of 65.44%. Conceptualization, S.L., S.P. and Y.J. ; methodology, S.L., validation, S.L., M.K., S.S. and S.B. ; data curation, M.K., S.S. and S.B. All authors have read and agreed to the published version of the manuscript. This work was supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2017-0-00050, Development of Human Enhancement Technology for auditory and muscle support). Not applicable. Not applicable. Not applicable. Not applicable. Data Availability Statement: Publicly available datasets were analyzed in this study. This data can be found here: [[http://dcase.community/challenge2021/task-acoustic-scene-classification](http://dcase.community/challenge2021/task-acoustic-scene-classification)]([http://dcase.community/challenge2021/task-acoustic-scene-classification](http://dcase.community/challenge2021/task-acoustic-scene-classification)) (accessed on 15 December 2021). The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results. Abbreviations The following abbreviations are used in this manuscript: