## 4 Results and Evaluation
### Subjective Tests  
We present an evaluation based on listening to audios. In this case, we have created a set of audios that comprises the four use cases that have been listened to by a group of people to evaluate the amount of background noise that has been suppressed. The dataset consists of 22 different audios; for each audio, the volunteers must listen to the audio with background noise and the same audio after being processed by our model. For each of the four use cases, there were four different audios, totaling 20 audios. The two remaining audios correspond to control audios where no background noise was eliminated and have been used to check that the surveyed people were performing the test well.  
During the process, volunteers could listen to each pair of audios as often as possible. Then, they had to choose between four different options: \"No or practically no noise eliminated\", \"Some noise eliminated\", \"Much noise eliminated\", or \"All or almost all noise eliminated\". The evaluation was delivered through a Google Form and 61 people answered it. All the information has been compiled in Figure 3. The top pie chart represents the average results of the evaluation of the 20 valid audios. The other pie charts depict the evaluation results for each of the use cases.  
Looking at the figure above, we can conclude that most people think that the model could suppress much or almost all or the noise (90%). Only in a few cases (10%), did the evaluation result in no noise eliminated. If we look at the use cases separately, there are no big differences in the obtaining of good evaluations (from much noise to all noise) with percentages of around 90%. It should be highlighted that the wind scenario is the one with more cases of removing all the noise, which corroborates the objective evaluations obtained with the mathematical metrics (the margin for improvement is less than in the other cases). If we look at the scenarios considering that as much noise as possible is eliminated, storms/wind is the one that performs the best, but the differences are not remarkable. Another interesting point is that wind/storms, rain, and traffic are the scenarios that have reported cases where no noise was eliminated.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Use Case** & & **MSE** & **SSIM** \\ \hline \multirow{4}{*}{Sotorms and rain} & Noisy audio & 1628 \(\pm\) 1246 & 0.6173 \(\pm\) 0.1257 \\  & Restored audio & 680 \(\pm\) 323 & 0.6257 \(\pm\) 0.1012 \\  & **Absolute difference** & **948** & \(\mathbf{-0.0084}\) \\  & **\% reduction** & **58.2\%** & \(\mathbf{-1.4\%}\) \\ \cline{2-4}  & Noisy audio & 1867 \(\pm\) 1100 & 0.5780 \(\pm\) 0.0848 \\  & Restored audio & 698 \(\pm\) 267 & 0.6075 \(\pm\) 0.0754 \\  & **Absolute difference** & **1169** & \(\mathbf{-0.0375}\) \\  & **\% reduction** & **62.6\%** & \(\mathbf{-5.1\%}\) \\ \cline{2-4}  & Noisy Audio & 1329 \(\pm\) 1139 & 0.6282 \(\pm\) 0.1127 \\  & Restored audio & 609 \(\pm\) 349 & 0.6437 \(\pm\) 0.0891 \\  & **Absolute difference** & **720** & \(\mathbf{-0.0155}\) \\  & **\% reduction** & **54.2\%** & \(\mathbf{-2.5\%}\) \\ \cline{2-4} Wind & Noisy audio & 683 \(\pm\) 541 & 0.7287 \(\pm\) 0.1107 \\  & Restored audio & 429 \(\pm\) 174 & 0.7311 \(\pm\)0.0769 \\  & **Absolute difference** & **209** & \(\mathbf{-0.0024}\) \\  & **\% reduction** & **37.2\%** & \(\mathbf{-0.3\%}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison between spectrograms.