## 3 MBN Signals Time-Frequency Transformation and Feature Extraction
The \(BN_{\text{CoV}}\) expresses the ratio of the mean and the standard deviation values and the observed monotonic increase of its value from TD to RD direction confirms previous observations. The last presented feature distribution (Figure 4d) relates to the statistical quantity of the spectrograms--the median value \(BN_{\text{Median}}\). The \(BN_{\text{Median}}\) characteristic is well corelated with the concentration measure \(BN_{\text{CM}}\) feature. This supports prior findings that at angles congruent with or near the RD direction, spectrogram's value essentially grows over the whole \(TF\) space. The resulting distributions allow for the same conclusions to be made. A general indication of the directions of high and low MBN activity can be seen; these directions are also consistent with the directions of easy and hard magnetization, respectively. Figure 5 presents the angular characteristics of a single selected feature, \(BN_{\text{CM}}\), for three selected samples of different grades: 0.30 mm thick CGO steel C\({}_{30\text{LSR1}}\), 0.30 mm thick laser scribed CGO C\({}_{30\text{LSR1}}\) and 0.27 mm thick nitridded CGO steel C\({}_{27\text{N}\text{E}}\). All distributions were normalized to the greatest value from all distributions. At first, one can notice the significant difference in the \(BN_{\text{CM}}\) value levels between samples. Furthermore, for each sample, a different course of characteristic is obtained. In the case of C\({}_{30\text{LSR1}}\) (a red-dotted chart), one can observe a monotonic, generally constant, growth of the feature's value, starting from the TD direction and heading into the RD direction, with the maximum close to the easy magnetization axis. The characteristic of the \(BN_{\text{CM}}\) for the C\({}_{30\text{LSR1}}\) laser scribed  
Figure 4: Polar charts (normalized to (0–1) range) of four exemplary time-frequency features extracted from MBN \(TF\) spectrograms obtained for selected sample C\({}_{27\text{LSR1}}\): (**a**) \(BN_{\text{CM}}\), (**b**) \(BN_{\text{SE}}\), (**c**) \(BN_{\text{CoV}}\), (**d**) \(BN_{\text{Median}}\). CGO (yellow-dotted chart) takes much smaller values compared to the results obtained for other presented in the figure; however, as in the first case, the distribution presents a relatively narrow angular range where the greatest feature's value occurred. In case of the nitrided CGO C\({}_{\text{27 Nn2}}\) (purple-dotted line), a great increase in value of the feature in the angular range from 0\({}^{\circ}\) to about 60\({}^{\circ}\) can be noticed, while after reaching the greatest value, a gentle decrease in the value is further observed while approaching the 90\({}^{\circ}\) angle. Due to the various characteristics of the features, it is necessary to apply an appropriate decision-making method. It is possible to use an expert to make this decision but then there is a human factor that can cause erroneous predictions (emotions, not-well physical condition, random happenstance). As a substitute for this, an algorithmized decision-making mechanism in the form of a machine learning model can be used. The next part of the paper will present the methodology for conducting research using machine learning algorithms, which will be described, and a summary of the results obtained will be presented.