## 4 Experiments and Results  
In this section, the proposed clustering and analysis methodology was applied to two acoustic environments (suburban and urban) and two types of day (weekday and weekend day) from the DYNAMAP project. The suburban environment consisted of \(N^{\prime}=19\) sensors deployed along the A90 circular highway surrounding the city of Rome (see Figure 2), while the urban scenario was defined with \(N^{\prime}=24\) sensors within District 9 of the city of Milan (see Figure 3). The acoustic data corresponded to two WASN-based audio databases collected through the two networks in real-time operation [10,11]. Both databases included data from two days in 2017 with different traffic conditions, one from a weekday (on Tuesday, the 28 th of November for the urban area, and on Tuesday, the 2 nd of November for the suburban environment), and another during the weekend (on Sunday, the 3 rd of December for the urban area, and on Sunday, the 5 th of November for the suburban environment). The audio recordings were collected from the first \(T=20\) min of each hour (with a sampling frequency of 48 kHz), a trade-off between node storage and communication capabilities. The set of sampled 1-h periods corresponded to hours 01:00, 03:00, 05:00, 07:00, 09:00, 11:00, 13:00, 15:00, 17:00, 19:00, 21:00 and 23:00 in the suburban environment (H = 12), to 02:00, 03:00, 05:00, 08:00, 09:00, 11:00, 14:00, 15:00, 17:00, 20:00 and 23:00 for the urban scenario during weekdays (H = 11), and to 02:00, 05:00, 08:00, 11:00, 14:00, 17:00, 20:00, 21:00 and 23:00 during weekends (H = 9) [10,11]. Nevertheless, due to different technical problems, one sensor per acoustic environment (hb114 and hb119 for the urban and suburban area, respectively) was discarded as the acoustic data provided was incomplete. As a result, 129 h 23 min of audio data from the \(N=18\) sensors of the suburban environment and 114 h and 43 min from the \(N=23\) sensors installed across the urban area were considered, respectively, for subsequent analyses.  
Regarding the computation of STPs, on the one hand, the recorded audio signals of \(T=20\) min length per sampled hour are parameterised by extracting \(B=48\) energy sub-bands at the frame level using 30 ms length Hamming windows with 50% of overlap. On the other hand, the STP value of a missing point was obtained through the cubic interpolation of the values at neighboring grid points in each respective dimension, that is, in frequency and time, following [19].  
As for the clustering analysis, an agglomerative hierarchical clustering technique was applied using [PERSON]'s minimum variance algorithm [30], as it allowed both the automatic grouping of STPs and interpretation of the resulting grouping through the derived hierarchical dendrogram. The clustering analysis considered a sweep between \(Q_{I}=2\) and \(Q_{F}=18\) clusters for the suburban area, and from \(Q_{I}=2\) to \(Q_{F}=23\) clusters for the urban environment, being \(Q_{F}\) in both cases the total number of operative sensors per area. Four CVIs were considered to determine \(Q^{*}\): (i) the ratio of within-cluster and between-cluster distances of the Davies-Bouldin index [31]; (ii) the Silhouette index [32], a measure of how similar an object is to its own cluster (cohesion) compared to other clusters (separation); (iii) the Gap value [33], which compares the within-cluster dispersion with that expected over an appropriate reference null distribution; and, (iv) the overall within-cluster variance of the Calinsky-Harabasz criterion [34].