## 3 Results and Discussion  
This section presents and discusses the results obtained in the comparison between trained models for the different ANN architectures. This evaluation was carried out in three approaches: global performance, time slot, and environmental acoustic pattern. First, the performance of the different models on the test datasets was calculated using Accuracy as a global metric. Table 3 shows the Accuracy of the 192 trained models for the eight ANNs defined in Section 2.4 on the test subsets of the 24 datasets representing each hourly time slot, where time slot \(X\) corresponds to the interval from X:00 hour to X:59 hour, as defined in Section 2.3. The global performance of the models depends on the time slot and the model, as expected; \(Net\_64\_32\_4\), from 21:00 to 21:59, shows the highest Accuracy at 0.6943, resulting in the best combination of architecture and hourly time slot. This is a particular insight, very valuable for city managers; however, this asseveration is difficult to generalize for other cities. Analyzing these results, a discussion is provided in the following paragraphs in order to obtain more general conclusions. Due to the existence of four categories, adopting a random model supposes an Accuracy of 0.25 in each of them. As shown in Table 3, in general, all models across every hour exceed the random model except one. As the pattern categories are not equally distributed (see Table 1), a baseline model could be the selection of the most representative category with an Accuracy of 0.39 (=27/70). Even though the Accuracy of the models ranges from 0.150 to 0.694, 160 of the 192 models (83%) have an Accuracy higher than the baseline model. In addition, a one-sided parametric hypotheses testing was carried out with the hypotheses represented in Equation (9):  
\[\left\{ \begin{aligned} & H_{0}:\mu\leq 0.39\\ & H_{1}:\mu>0.39\end{aligned} \right. \tag{9}\]  
Considering the central limit theorem, the test statistic follows a Student's t-distribution with 191 degrees of freedom, and the estimator of the test is 18.929, equivalent to a \(p\)-value \(<2.2*10^{-16}\). Therefore, the null hypothesis is rejected, leading to the conclusion that the improvement when using an ANN to estimate the long-term environmental acoustic pattern of a spot based on short-term data is statistically significant. Regarding the optimum hourly time slot to capture data that best represent the long-term pattern, Table 3 shows that on average every hourly time slot improves the baseline model; the better hourly time slots to predict environmental acoustic behaviors are 14, 17, 20, 4, and 21, in which the averaged Accuracy is higher than 0.55. Finally, the worst time slots to capture data are 7, 10, 8, 12, 22, and 11, in which the Accuracy is lower than 0.49. However, outliers decrease the representativeness of the mean value. Therefore, a median Accuracy analysis was performed to minimize the impact of outliers in the above results. The Accuracy distribution for each hourly time slot ordered by the median of the Accuracy is shown in Figure 3. These hourly time slots are colored yellow for daytime periods (from 7:00 a.m. to 7:00 p.m.), orange for evening periods (from 7:00 p.m. to 11:00 p.m.), and gray for night periods (from 11:00 p.m. to 07:00 a.m.), as defined in Directive 2002/49/EC [2]. Figure 3 shows that 21, 18, and 17 are the best hourly time slots, in this order. On the other hand, the worst time slots are 22, 8, and 12. The top seven most accurate hourly time slots are in the interval 14 to 21. From this interval, only 19 is not in the ranking, falling to the twelfth position. Therefore, the period from 14:00 to 22:00 is recommended to capture data and estimate the location acoustic pattern. \begin{table}
\begin{tabular}{l|c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{10}{c}{**Hourly Time Slot**} \\ \cline{2-13}  & **0** & **1** & **2** & **3** &  
Next, the impact of the complexity of the ANN architecture on the performance in the classification was analyzed.