## 4 The Applicability of the Prediction Model to Different Kinds of Earthmoving Machines  
In order to verify whether the annoyance prediction model is able to assess the annoyance conditions at the work position of any kind of earthmoving machines, all six regression equations reported in Table 1 (those originally used to develop the model for compact loaders) were considered. For each equation, \(Y\) represents the predicted annoyance value that must be calculated using the objective parameters of the six noise stimuli. These equations were applied to the six sound stimuli recorded from the different kinds of earthmoving machines listed in Table 2. The calculation was repeated for the left and the right noise stimuli, separately. As similar results were found for both, only those of the right noise stimulus will be presented hereinafter. Figure 2 shows the predicted values of annoyance plotted against the observed values (subjective scores) for each equation. The match of the data (predicted vs. subjectively judged) was assessed by means of the squared value of the correlation coefficient (\(R^{2}\)): the better the correlation, the higher the \(R^{2}\) value. At first glance, all these results seem to be a kind of \"compromise solution\" as the \(R^{2}\) values are all lower than 0.8. However, a more careful analysis shows that all these numerical prediction models \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Machine**} & \multirow{2}{*}{**A**} & \multirow{2}{*}{**B**} & \multirow{2}{*}{**C**} & \multirow{2}{*}{**D**} & \multirow{2}{*}{**E**} & \multicolumn{6}{c}{**Subjective Annoyance Score**} \\ \cline{5-10}  & & & & & & & **Sum** & **Rank** & **\%** \\ \hline A & - & 22 & 32 & 24 & 31 & 21 & 130 & 1 & 81.3 \\ B & 10 & - & 32 & 10 & 31 & 12 & 95 & 4 & 59.4 \\ C & 0 & 0 & - & 0 & 5 & 0 & 5 & 6 & 3.1 \\ D & 8 & 22 & 32 & - & 31 & 13 & 106 & 3 & 66.3 \\ E & 1 & 1 & 27 & 1 & - & 0 & 30 & 5 & 18.8 \\ F & 11 & 20 & 32 & 19 & 32 & - & 114 & 2 & 71.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Overall matrix of the sound stimuli as to the subjective annoyance score (SAS). lead to an incorrect assessment of annoyance for Stimulus F. This signal was ranked second as to the annoyance score given by the subjects, while all numerical predictions lead to lower annoyance values. It is worth emphasizing that the frequency content of this signal is very unusual for earthmoving machines, as it has an unbalanced weight between the noise contributions at low (combustion process at 50 Hz) and medium-high (hydraulic and engine cooling systems at 400-4000 Hz) frequencies. This peculiarity could suggest considering Stimulus F as an \"outlier\" and to exclude it from the data set. Figure 3 shows the regression curves and the \(R^{2}\) values calculated with and without Stimulus F. This quantifies how good the relationship is between the annoyance calculated by means of a prediction model and that assessed by means of subjective evaluations. This is repeated for each of the equations in Table 1 (\(Y_{1}\), \(Y_{2}\), \(Y_{3}\), \(Y_{4}\), \(Y_{5}\), \(Y_{6}\)). Results show that all the regression equations lead to an \(R^{2}\) value higher than 0.87 when the Signal F is not included in the data set. In particular, \(Y_{3}\) and \(Y_{5}\) both have very high values almost equal to 1 (\(R^{2}\) = 0.997). On the basis of these considerations and results, it turns out that the annoyance prediction model developed for compact loaders (\(Y_{3}\), see Equation (1)) offered a good assessment of noise annoyance at the workplace for other kinds of earth moving machines. Its validity should be further assessed with a larger number of machines. Figure 3: _Cont_. Figure 2: Comparison of the predicted and observed values of annoyance with different numerical regression models. Figure 4 shows a comparison between the annoyance values predicted by equation \(Y_{3}\) and those obtained by subjective listening tests for all six stimuli.