## 3 Results and Discussions  
The first part of the study was used to collect several examples sufficient to train the algorithm effectively. Ten anechoic signals with different characteristics were used to excite the rooms in a different way, to record the response of the rooms to the different sound stresses. An anechoic signal is recorded in an anechoic chamber in which there are no reflections as the walls, the floor and the ceiling absorb all the incident waves. Different sounds with different characteristics were used to ensure an effective generalization capacity for the model. The characteristics of the signals used are such as to cover all the frequencies of the audible spectrum (20 Hz-20 kHz). Table 3 reports the statistical descriptors of the anechoic signals used in this work [40]. Leq (equivalent continuous sound pressure level) is the constant noise level that would result in the same total sound energy being produced over a given period. Lmax (maximum level) is the noise with maximum energy content. Lmin (minimum level) is the noise with minimum energy content. StdDev is the standard deviation. L95, L90, L50, L10, L5 are the percentile sound levels. The percentile level represents the noise level exceeded for a percentage of the measurement time. For example, L95 represents the noise level exceeded for 95% of the measurement time. Figure 4 shows the average spectral levels in the one-third octave band between 100 Hz and 16 kHz (dB Lin) for the ten signals used to train and test the models. The first three signals (white noise, pink noise, brown noise) represent artificially generated noise with characteristics that cover the entire frequency spectrum. White noise is characterized by the absence of periodicity and by a constant amplitude that includes all the frequencies of the audible. Pink noise has low frequency components with higher power. Finally, \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Signal** & **Leq** & **Lmin** & **Lmax** & **StdDev** & **L95** & **L90** & **L50** & **L10** & **L5** \\ \hline White noise & 71.3 & 70.9 & 71.7 & 0.1 & 70.9 & 71 & 71.2 & 71.4 & 71.5 \\ Pink noise & 64.8 & 64 & 66.7 & 0.4 & 64.1 & 64.2 & 64.6 & 65 & 65.2 \\ Brown noise & 61.2 & 59.7 & 64.3 & 0.6 & 60.2 & 60.5 & 61 & 61.8 & 62.3 \\ Sweep noise & 75.9 & 63.4 & 81.6 & 4.1 & 66 & 69.3 & 74.5 & 78.8 & 80.6 \\ Music 1 & 62.1 & 46.7 & 68.2 & 5.4 & 49.6 & 51.9 & 58.9 & 66.1 & 67.2 \\ Music 2 & 58.4 & 47 & 65.4 & 4.3 & 49.4 & 50.5 & 56 & 61.7 & 63.7 \\ Speech & 63.9 & 44.7 & 70.9 & 6.2 & 47.5 & 51.7 & 62.1 & 67.2 & 68.4 \\ Music 3 & 66.3 & 54.8 & 69.5 & 1.7 & 64.1 & 64.4 & 66.1 & 67.6 & 67.9 \\ Music 4 & 70.5 & 52.8 & 76.1 & 4.8 & 59.5 & 62.9 & 68.6 & 74.4 & 75.1 \\ Music 5 & 68.5 & 46.7 & 76.5 & 6.2 & 55.3 & 57.3 & 63.1 & 73.6 & 75.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Training signals descriptors in dB. brown noise has an even greater accentuation of the presence of low frequencies than pink noise. The intensity decreases, from one octave to the other, like the inverse of the square of the frequency with an attenuation, on a logarithmic scale, of 6 dB. Sweep noise is a signal composed of a pure tone whose frequency increases over time with a certain trend. In addition, anechoic female speech and anechoic music signals were used, specifically the music signals concerned various musical instruments with contributions to both low and high frequencies. The diversification of the signals was a choice dictated by a strategy aimed at collecting records that contain the greatest number of information necessary to develop a model capable of generalizing the behavior of the system [41]. The elements present within an enclosed space (walls, furniture, people) affect the acoustic propagation with important effects on the acoustics of the environment itself as they absorb the sound energy that affects them to varying degrees. The acoustics of the room are also conditioned by the size of the room as well as by its geometry.