## 3 Results and Discussion  
The total number of generated datasets was 22 per spectrogram type, where the intensity of noise gradually grew from \(-10\) dB to \(+10\) dB with steps of 2 dB. To eliminate any statistical errors from the classification accuracy of a trained neural network, the training was performed multiple times for all datasets. The first experiments were done on datasets with images of spectrogram 1. Table 4 shows a list of classification accuracies, in percentages, that the CNN could reach from training on a particular dataset with data in the form of spectrogram 1. Graphical representation of the classification accuracy trend is detailed in Figure 10, which helps us to obtain a better image of the relationship between environmental noise and classification accuracy. The same experiment was done on datasets with data in the form of spectrogram 2. Numerical value accuracy rates are presented in Table 5 and the graphical trend is shown in Figure 11.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \(-\mathbf{1  
Figure 11: Trend of classification accuracy across all tested intensities of EN for data represented by spectrogram 2. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \(-\)**10 dB** & \(-\)**8 dB** & \(-\)**6 dB** & \(-\)**4 dB** & \(-\)**2 dB** & **0 dB** & **+2 dB** & **+4 dB** & **+6 dB** & **+8 dB** & **+10 dB** \\ \hline
1 st run & 97.605 & 94.910 & 95.509 & 95.210 & 94.311 & 94.611 & 85.629 & 81.437 & 50.000 & 50.000 & 50.000 \\ \hline
2 nd run & 96.707 & 94.012 & 95.210 & 94.910 & 94.311 & 93.413 & 85.928 & 82.934 & 55.090 & 50.000 & 50.000 \\ \hline
3 rd run & 96.407 & 94.311 & 95.808 & 94.910 & 94.012 & 92.814 & 86.826 & 80.539 & 50.000 & 50.000 & 50.000 \\ \hline
4 th run & 96.407 & 94.611 & 95.210 & 95.808 & 94.910 & 93.114 & 88.024 & 79.341 & 50.000 & 54.790 & 50.000 \\ \hline
5 th run & 97.305 & 95.210 & 95.808 & 95.509 & 94.611 & 93.413 & 87.425 & 79.042 & 51.198 & 50.000 & 50.000 \\ \hline
6 th run & 96.707 & 94.311 & 94.910 & 95.808 & 94.611 & 93.114 & 87.725 & 79.940 & 50.000 & 50.000 & 50.898 \\ \hline
7 th run & 97.006 & 94.611 & 94.910 & 95.509 & 94.910 & 93.413 & 85.928 & 79.042 & 50.000 & 50.000 & 50.000 \\ \hline
8 th run & 96.407 & 94.311 & 95.808 & 95.808 & 94.611 & 94.012 & 86.527 & 80.240 & 50.000 & 50.000 & 50.000 \\ \hline
9 th run & 96.407 & 94.910 & 94.910 & 94.311 & 94.311 & 87.725 & 79.341 & 50.000 & 51.497 & 50.000 \\ \hline
10 th run & 97.006 & 95.210 & 95.210 & 94.910 & 94.311 & 94.311 & 87.126 & 81.737 & 50.000 & 50.000 & 50.000 \\ \hline avg. & 96.796 & 94.641 & 95.329 & 95.269 & 94.491 & 93.563 & 86.886 & 80.359 & 50.629 & 50.629 & 50.090 \\ \hline \end{tabular}
\end{table} 
Table 5: Obtained classification accuracies [%] for datasets of spectrogram 2. Figure 10: Trend of classification accuracy across all tested intensities of EN for data represented by spectrogram 1. The results describe the influence of environmental noise on the assembly line to the classification accuracy of a quality control algorithm based on a convolutional neural network. Both graphs show that decreasing the intensity of environmental noise from the nominal value leads to slight improvements of classification accuracy, and these results are very stable across multiple runs. On the other hand, increasing the same amount of the noise level has a greater impact on reducing accuracy rates. Sound recordings transformed to spectrograms 1, which have greater window length, tend to decrease the accuracy rate with noise increasing more or less linearly. On the contrary, a neural network trained on data transformed by spectrograms 2 could process slightly noisy records, but from +6 dB of nominal noise level, it could not learn any credible decision-making criterion, thus confirming the classical analytical method results. Simply put, the noise on this kind of spectrogram tends to overdraw the signal components' expressions more aggressively.