## 2 The Annoyance Prediction Model for Compact Loaders  
The annoyance prediction model was developed starting from measurements on 41 compact loaders belonging to six families both in dynamic and stationary conditions in order to represent all possible operations of such machines [18]. When the machine was in a stationary idle condition, the binaural signals were recorded by means of a head and torso simulator placed in the operator station. When the machine was performing a simulated work cycle (charging and discharging gravel or loam from a stockpile to another),the binaural signals were recorded by means of a binaural headphone with miniature microphones placed at the entrance of the operator's ear canals [19]. A total amount of 62 binaural noise signals were then available. The following acoustic and psychoacoustic parameters were calculated for all these signals, using the Pulse Sound Quality software (version 10, type 7698, Bruel & Kjaer, Narum, Denmark): overall sound pressure levels, percentile values of the sound pressure levels, overall and percentile values of loudness (\(N\)) and sharpness (\(S\)), overall values of roughness (\(R\)), and fluctuation strength (\(Fl.St.\)). This huge amount of sound stimuli was divided into nine groups; for each of them, the subjective assessment of annoyance was obtained by means of subjective listening tests carried out according to the paired comparison procedure [20]. Multiple regression analysis was then used for developing the prediction model as this technique is the most commonly used for analyzing multiple dependence between variables [21; 22]. Six groups of noise stimuli were used in the development phase, while the remaining three groups were used for validation purposes. The set of predictor variables which led to the highest \(R^{2}\) (squared value of the correlation coefficient between the subjective scores and the predicted values of annoyance) when applied to the six groups of sound stimuli was (Peak, \(N_{50}\), \(S_{5}\)), i.e., the sound pressure Peak level, in dB, the fiftieth percentile of loudness in some, \(N_{50}\), and the fifth percentile of sharpness in acum, \(S_{5}\). The multiple regression equations obtained for the set of variables (Peak, \(N_{50}\), \(S_{5}\)) for each group of sound stimuli are listed in Table 1.  
For each noise group, this set of variables accounted for at least 89% of the variation in the subjective scores, with the only exception of Noise Group 1. Each regression equation was finally applied to the other five groups, and the predicted annoyance values were calculated for each equation. The correlation between these predicted annoyance values and the subjective ratings was evaluated for each noise group: the better the correlation, the higher the \(R^{2}\) value. In such a way, the best annoyance prediction model was identified as the one that gave the maximum sum of \(R^{2}\) over all the noise groups except for the one from which that model was issued. According to this criterion, the regression equation referred to the Noise Group 3 gave the best results, and it was chosen as the numerical prediction model able to assess noise annoyance at the workplace of compact loaders:  
\[Y_{3}=5.322+0.038\;\text{Peak}+0.057\;N_{50}+0.412\;S_{5} \tag{1}\]  
where \(Y_{3}\) indicates the predicted annoyance value. The validation process performed on the remaining three groups of sound stimuli confirmed the reliability of this model as an alternative and simpler way for manufacturers and customers to assess the grade of annoyance at the workplace of any compact loader.