## Appendix A  
The Appendix contains the results for the best models for all datasets, before and after using complexity reduction. The complete results of all models can be obtained by contacting the author. _Used Programming Libraries_  
The code was written in Python 3.9 [23]. For the XGB and ANN model creation, the scikit-learn library [24] was used. This library uses the original library for the implementation of the XGBoost models [13]. For data processing, pandas was used [25]. For the visual representation of the XGB model results, SHapley Additive exPlanations, (SHAP) was used [17; 26] in combination with Matplotlib [27]. The librosa library was used to create the mfcc evaluations [28]. For the FFT evaluations and many smaller calculations, numpy was used [29]. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Preprocessing** & **No. of** & **Names** \\ \hline Fast Fourier Transformation & 4800 bins of 4 Hz each & 4800 & fft\_00004\_Hz to fft\_19200\_hz \\ \hline Mel Frequency Cepstral Coefficient Analysis & & mean\_mfcc01... 05 \\ mean, median, stdev, max, and min & 25 & stddev\_mfcc01... 05 \\ for five coefficients & & max\_mfcc01... 05 \\  & & min\_mfcc01... 05 \\ \hline Time-Based Analysis & & 21\_i\_a\_sum\_max\_posneg \\ a: absolute acceleration & & 22\_i\_a\_sum\_max\_pos \\ sum: sum over interval (i) or bar (b), & & 23\_i\_a\_sum\_max\_neg \\ max: maximum; pos: positive; neg: negative & 7 & 24\_i\_a\_max\_pos \\ Example: 21\_i\_a\_sum\_max\_posneg means ”feature 21, & & 25\_i\_a\_max\_pos \\ absolute sum over all positive and negative maxima & & 26\_i\_i\_a\_mean\_max\_pos \\ over all hammer blows of the examined interval” & & 27\_i\_a\_mean\_max\_neg \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Signal-processing methods and resulting features. \begin{table}
\begin{tabular}{c c c} \hline \hline Filter Fractions in Percent: & 100, 90, 80, 70, 60, 50, 40, 30, 20, 10, 7, 4, 1 \\ \hline XGB-parameter & value(s) \\ \hline n\_estimators & 2000 \\ early\_stopping\_rounds & 50 \\ eta & 0.1 \\ parallel tree & 1, 3, 5, 7 \\ max depth & 2, 4, 6 \\ min child weight & 1 \\ subsample & 0.6 \\ colsample\_bytree & 0.7 \\ colsample\_bylevel & 0.7 \\ colsample\_bynode & 0.7 \\ alpha & 0.0001 \\ lambda & 1 \\ gamma & 0 \\ random seed & 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Filter fractions and model parameters with values in the model calculations. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline
**Dataset** & **DS 9** & **DS 1** & **DS 11** & **DS 3** & **DS 7** & **DS 5** & **DS 12** & **DS 10** & **DS 4** & **DS 2** & **DS 8** & **DS 6** \\
**Global Rank** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** \\ \hline mean RMSE (-) & 0.093 & 0.098 & 0.098 & 0.099 & 0.114 & 0.117 & 0.127 & 0.133 & 0.138 & 0.142 & 0.147 & 0.153 \\ \hline best RMSE (-) & 0.069 & 0.070 & 0.070 & 0.073 & 0.081 & 0.090 & 0.080 & 0.077 & 0.079 & 0.080 & 0.122 & 0.126 \\ \hline mean MAE (g) & 5.78 & 5.83 & 5.83 & 5.85 & 7.26 & 7.44 & 8.41 & 9.07 & 9.23 & 9.59 & 9.25 & 10.1 \\ \hline best MAE (g) & 4.90 & 4.39 & 4.39 & 4.84 & 5.35 & 5.74 & 5.66 & 6.06 & 5.77 & 5.75 & 8.12 & 6.73 \\ \hline mean DM (g) (of 10\% test data) & 24.7 & 24.7 & 24.7 & 24.8 & 24.8 & 24.8 & 28.8 & 28.1 & 29.0 & 29.0 & 29.8 & 30.2 \\ \hline mean MAE, relative (\%) & 23 & 24 & 24 & 30 & 30 & 29 & 32 & 32 & 33 & 31 & 34 \\ \hline best MAE, relative (\%) & 19 & 18 & 18 & 20 & 22 & 23 & 21 & 22 & 21 & 21 & 26 & 25 \\ \hline filter fraction (\%) & 40 & 90 & 50 & 100 & 7 & 4 & 30 & 30 & 30 & 30 & 4 & 1 \\ \hline extended features & & & & & & & x & x & x & x & x \\ \hline composition & & & & & x & & & x & & & \\ features & & & & & & & & x & & & & \\ \hline signal processing & mfcc & none & time- & based & none & FFT & all & time- & based & mfcc & none & none & FFT & all \\ \hline \hline \end{tabular}
\end{table}