## 4 Conclusions  
An experimental study was carried out that involved taking acoustic measurements in the streets of Caceres (Spain) at the same time as administering pedestrian surveys, in order to study the ability of acoustic variables related to sound energy in urban environments to estimate the occurrence and importance of noise effects. The results obtained in the present study showed that many of the noise effects studied here were significantly correlated with a large proportion of these acoustic variables, reaching values for the correlation coefficient of above 0.8 in some cases. The findings of the study indicate that the average energy of the sound wave over the full spectrum is an important sound characteristic in terms of explaining the variability of effects such as irritability (a), startle (b), raising the volume of the conversation in situ (e), a noisy street (h), and annoyance (i). For almost all of the effects analysed here, LZeq or Lceq were better than or similar to Lceq in terms of explaining their variability. Moreover, a time I weighting did not have any significant influence on the values of the correlation coefficients for any of the subjective variables. Other indicators that consider different adaptations depending on the sound level, such as loudness, loudness level, NR, NC, and NCB, did not generally provide an improvement, except for interrupting a phone conversation (f), raising the volume of their voice on a phone conversation (g), and annoyance (i). The indicators based on the minimum sound levels analysed here explained a greater proportion of the variability in the noise effects than the indicators of maximum energy. They were even better for explaining the variability in startle (b) and annoyance in the ears (c), or equivalent for interrupting a phone conversation (f), to all of the average energy \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **(b)** & **(c)** & **(d)** & **(e)** & **(f)** & **(g)** & **(h)** & **(i)** \\ \hline (a) & \(-0.01\)\({}^{\text{n.s.}}\) & \(-0.38\)\({}^{\text{*}}\) & \(0.56\)\({}^{\text{***}}\) & \(-0.38\)\({}^{\text{*}}\) & \(0.53\)\({}^{\text{**}}\) & \(0.32\)\({}^{\text{n.s.}}\) & \(-0.39\)\({}^{\text{*}}\) & \(0.31\)\({}^{\text{n.s.}}\) \\ (b) & & \(0.70\)\({}^{\text{***}}\) & \(-0.08\)\({}^{\text{n.s.}}\) & \(0.29\)\({}^{\text{n.s.}}\) & \(-0.07\)\({}^{\text{n.s.}}\) & \(-0.02\)\({}^{\text{n.s.}}\) & \(-0.03\)\({}^{\text{n.s.}}\) & \(-0.52\)\({}^{\text{**}}\) \\ (c) & & & \(-0.38\)\({}^{\text{*}}\) & \(0.31\)\({}^{\text{n.s.}}\) & \(-0.50\)\({}^{\text{**}}\) & \(-0.41\)\({}^{\text{*}}\) & \(0.60\)\({}^{\text{***}}\) & \(-0.68\)\({}^{\text{***}}\) \\ (d) & & & & \(0.11\)\({}^{\text{n.s.}}\) & \(0.73\)\({}^{\text{***}}\) & \(0.54\)\({}^{\text{**}}\) & \(-0.30\)\({}^{\text{n.s.}}\) & \(0.60\)\({}^{\text{***}}\) \\ (e) & & & & & \(0.24\)\({}^{\text{n.s.}}\) & \(0.55\)\({}^{\text{**}}\) & \(0.37\)\({}^{\text{*}}\) & \(0.25\)\({}^{\text{n.s.}}\) \\ (f) & & & & & & \(0.90\)\({}^{\text{***}}\) & \(-0.45\)\({}^{\text{**}}\) & \(0.74\)\({}^{\text{***}}\) \\ (g) & & & & & & & \(-0.29\)\({}^{\text{n.s.}}\) & \(0.74\)\({}^{\text{***}}\) \\ (h) & & & & & & & & \(-0.23\)\({}^{\text{n.s.}}\) \\ \hline \hline \end{tabular} \({}^{\text{n.s.}}\) Non-significant correlation (\(p>0.05\)). \({}^{\text{*}}\) Significant at \(p\leq 0.05\). \({}^{\text{**}}\) Significant at \(p\leq 0.01\). \({}^{\text{***}}\) Significant at \(p\leq 0.001\).  
\end{table} 
Table 7: Correlation coefficients between the values of the correlation coefficients of variables (a)–(i) in Figure 4.  
indicators. In regard to the minimum energy sound indicators, it seems that the C weighting gives better results than the A weighting for all the noise effects.