## 3 Results  
### From Continuous to Global Perceived Pleasantness Assessment  
#### 3.1.1 First Experiment  
Figure 3 depicts, for each of the 16 sequences presented in Section 2.4.1, the 1 s sound pleasantness (\(P\)) evolution (mean values and standard deviation for the 25 participants) and the 1 s sound level. The combination of the initial sequences \(\alpha\) and \(\beta\) resulted in 16 sequences, with a large variety. Table 2 shows the average pleasantness, of both the participants and over time (average of the 125 ms mean pleasantness ratings over the 3 min), the average global sound pleasantness (\(GP\)) of the participants, and the difference between both values (\(\Delta GP\)-\(P_{\rm mean}\)), for each of the 16 sequences. \begin{table}
\begin{tabular}{c c c} \hline
**Street Name** & **Description** & **Labels** \\ \hline Rue de Tolbiac & Large street & T \\ Passage Vendrezanne & Pedestrian Street & V \\ Avenue Blanqui & Avenue & Bl \\ Jardin Brassai & Park & Br \\ Avenue Italie & Large Avenue & I \\ Rue des 2 avenues & Pedestrian street & D \\ Parc de Choisy & Park & ChP \\ Rue du Moulinet & Street & M \\ Avenue Choisy & Large Street & ChA \\ \hline \end{tabular}
\end{table} 
Table 1: Short description and labels for the travelled sound environments. Figure 3: Continuous perceived pleasantness, mean values over participants \(P_{\rm mean}\) (thick black line), standard deviations (light black lines), and sound level (\(L_{\rm eq,1s}\), purple) over time for the 16 sequences. There is a statistically significant difference in \(\Delta\)_GP-P_mean between the sequences, confirmed by a one-way ANOVA (\(F\)(15,351) = 2.84, \(p\) < 0.001). Table 2 shows that, for sequences that mainly consist of a boulevard interrupted by a park sequence \(\alpha\) (for example, compare sequences A1, A2, A3 and A4), and the more that \(\alpha\) appears near the end of the sequence, the more the \(\Delta\)_GP-P_mean significantly increases (\(F\)(3,88) = 4.78, \(p\) < 0.01). Inversely, although less pronounced, for sequences that mainly consist of a park interrupted by the boulevard sequence (for example, compare sequences B1, B2, B3 and B4), and the more of the unpleasant environment that appears near the end of the sequence, the greater the difference in \(\Delta\)_GP-P_mean decreases. This trend is not significant (\(F\)(3,88) = 1.75, \(p\) = 0.16), but the difference in \(\Delta\)_GP-P_mean between the sequences B1 and B4 is significant (\(F\)(1,44) = 4.52, \(p\) < 0.05). Finally, the Ci sequences are significantly different to the Di sequences (\(F\)(1,135) = 20.63, \(p\) < 0.01). In order to investigate the apparent temporal effect when assessing the global sound pleasantness of a sound sequence, a multiple linear regression is constructed over all of the sound sequences. Four presumed factors are tested (mean value, trend effect, recency effect, and primacy effect), using the variables presented in Table 3. All of the variables are calculated over the averaged temporal curve. The best linear regression model is obtained through a stepwise procedure (Bidirectional elimination), maximizing the explained variance (In this paper, the explained variance corresponds to the adjusted \(R^{2}\)) at 95% (\(R^{2}\) = 0.95, \(F\)(2,14) = 136.0, \(p\) < 0.001). The function selects the variables \(P_{\rm mean}\) (\(b^{*}\) = 0.81, \(t\)(14) = 13.6, \(p\) < 0.001) and the final sound pleasantness \(P_{\rm end}\) (\(b^{*}\) = 0.45, \(t\)(14) = 7.6, \(p\) < 0.001), which corresponds to the arithmetic average of the sound pleasantness, collected during the last 30 s of the sequence (the sequences are constructed so that the last 30 s always have a stable sound environment). This model outperforms the _GP_ value, estimated with the unique predictor \(P_{\rm mean}\) (\(b^{*}\) = 0.87, \(t\)(14) = 6.5, \(p\) < 0.001), which explains only 74% of the variance (\(R^{2}\) = 0.75, \(F\)(2,14) = 42.8, \(p\) < 0.001).