## 1 Introduction
The major strength of STFT is the possibility to adjust the computational parameters for transformation operation to obtain a specific spectrogram character and resolution enabling further analysis in different ways. One of the computational parameters is the window, which slides throughout the signal, enabling frequency analysis of the signal bands for a specific time step. The type and size of the window can be adjusted. As the window size changes, the resolution in the time and frequency changes. The larger the window, the higher the resolution in frequency and the lower the resolution in time, and correspondingly, the smaller the window, the lower the resolution in the frequency and the higher the resolution in time as presented in Figure 2. These properties play a critical role in the adjustment of the transformation results to the physical nature of MBN signals. The course of the Barkhausen phenomenon is complex, it  
Figure 1: Example of MBN signal obtained during the examination of an GO electrical steel sheet: \(\alpha=0^{\circ}\) is the transverse direction of steel, while \(\alpha=90^{\circ}\) is the rolling direction of steel. can have local areas of increase and decrease in intensity (higher and lower MBN activity), and these phases can be associated with different aspects of structural changes in the material. Therefore, it can be considered holistically and locally. In each of these cases, there is a need to match the appropriate resolution in time and frequency, which enables using STFT. Articles [24; 25] present various ways of transformed signal analysis using different computational window configurations. When sub-periods of MBN burst are to be analyzed and the visibility of changes in TF space is important, a smaller window can be selected, as shown in article [25]. On the other hand, when a generalized and comprehensive analysis of the changes occurring in the spectrogram needs to be carried out, a larger [24] window can be chosen. This enables analyzing the hidden diagnostic information of MBN from various points of view and gaining knowledge about the different properties of materials. However, the greatest challenge arises in the proper interpretation of information encoded in a stochastic and non-deterministic signal such as MBN. In general, two approaches can be distinguished in the field of signal analysis and knowledge building from MBN signals. The first one, the most frequently used, is based on classical signal analysis and determination of signal features, often statistical ones, which are calculated from various representations of the signal, such as time, frequency, time-frequency or time-scale and the relationships between features distribution for various materials stages are analyzed [37]. Such an approach was presented in [38], where the MBN was observed for plastically deformed structural steel samples and statistical features were extracted from the time-frequency representations of the MBN signals. In this case, the features were plotted as a function of tension and adequate conclusions were drawn from the analysis. Another method of performing the analysis and interpretation of information is to use artificial intelligence (AI). There are two possibilities for accomplishing this approach at this point. Shallow neural networks and machine learning methods can be applied, or deep neural networks can be employed for analysis. In the case of the first approach, the calculated statistical features are provided as input parameters to a machine learning algorithm or a shallow neural network and then a classification or regression model is trained. This approach was used for the analysis of MBN signals acquired during the testing of electrical steel sheets [39]. The second group refers to utilization of deep neural networks. Recently, AI models in the form of 1-D or 2-D convolutional neural networks (CNN) were used to more accurately analyze the information contained in the MBN raw signal or their time-frequency representations.