## 3 Results and Discussions
To give the model its ability to generalize the behavior of the system, rooms with different characteristics have been selected both in terms of size, geometry, and furnishings. Twelve rooms with parallelepiped geometry were selected. The rooms are differentiated by volume, by type of flooring, by type of walls, and by type of furniture. Table 4 summarizes the main characteristics of the rooms. Figure 5 shows some of the rooms used in this study. The type of rooms used is diversified: We pass from rooms used as offices, to laboratories, conference rooms, mechanical workshops, and rooms in houses. In each room the reverberation time was measured using the impulse response technique in accordance with ISO 3382 [23]. The rooms were characterized from an acoustic point of view with the values of reverberation time T\({}_{30}\). Table 5 shows the octave band reverberation time measured in some of the rooms used in this study. Figure 4: Average spectral levels in the one-third octave band (dB Lin) between 100 Hz and 16 kHz (logarithm scale) for the ten signals used to train and test the models. As anticipated, the measured reverberation times were used for the construction of the dataset to be used as the input of the prediction model. Using these values, the features extracted from the recordings were appropriately labeled. Figure 6 shows the magnitude-squared coherence estimation of the recorded signals in twelve rooms, evaluated using the Equation (7). For each estimation, the same source signal was used (white noise). We can note that the coherence function allows us to discriminate between the different curves and therefore represents an appropriate choice as a descriptor. The results returned by a simulation model strongly depends on the quality of the input data that is presented to the algorithm. This is the reason why to obtain a satisfactory result it is necessary to pay particular care and attention to the procedure of processing the input data. In Figure 3 we have presented a flow chart of the entire procedure, in it is possible to notice that the first part of the procedure is aimed at building the dataset that will be used for training the model and subsequently for its validation and testing. We decided to use the spatial coherence function as a characteristic; specifically, the magnitude-squared \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Length (m)** & **Width** & **Height** & **V** & **Floor** & **Walls** & **Furniture** \\  & & **(m)** & **(m)** & **(m3)** & **Type** & **Type** & **Type** \\ \hline T\({}_{30}\), room 1 & 10.8 & 7.4 & 3.5 & 279.72 & PVC & plastered & Office \\ T\({}_{30}\), room 2 & 10.8 & 7.4 & 5 & 399.60 & PVC & plastered & Office \\ T\({}_{30}\), room 3 & 27.8 & 12 & 12 & 4003.20 & Concrete & plastered & Workshop \\ T\({}_{30}\), room 4 & 7.3 & 4.4 & 3.5 & 112.42 & marble grit & plastered & Laboratory \\ T\({}_{30}\), room 5 & 6.8 & 3.9 & 3.5 & 92.82 & marble grit & plastered & Office \\ T\({}_{30}\), room 6 & 4.4 & 3.1 & 3.5 & 47.74 & marble grit & plastered & Office \\ T\({}_{30}\), room 7 & 6.7 & 4.1 & 3.5 & 96.15 & marble grit & plastered & Office \\ T\({}_{30}\), room 8 & 5 & 3 & 2.7 & 40.50 & terracotta tiles & ceramic & Home \\ T\({}_{30}\), room 9 & 5 & 5 & 2.7 & 67.50 & stone tiles & plastered & Home \\ T\({}_{30}\), room 10 & 5 & 4 & 2.7 & 54.00 & stone tiles & plastered & Home \\ T\({}_{30}\), room 11 & 10 & 7 & 3.5 & 245.00 & concrete & plastered & Home \\ T\({}_{30}\), room 12 & 9 & 6.5 & 2.7 & 157.95 & stone tiles & plastered & Home \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Characteristics of the rooms covered by this study. Figure 5: Example images of some rooms used to record the response of the environment to the sounds played. The rooms have both geometric characteristics and different sizes. The volume of the rooms ranges from approximately 40 m\({}^{3}\) to more than 4000 m\({}^{3}\).