## 1 Introduction  
Non-destructive testing is becoming more and more increasingly applied not only in traditional and industrial solutions [1; 2; 3; 4], but also in the search for new, alternative methods of testing material properties [5; 6]. The rise of this type of testing saves time and reduces maintenance costs. A method that enables the examination of materials with ferromagnetic properties is the Magnetic Barkhausen Noise (MBN) method [7; 8]. The MBN method is sensitive in changes in the magnetic domain structure of a ferromagnetic material [9]. During the process of reorganization of the magnetic domain structure, there is a non-continuous movement of the domain walls as the magnetic domains vectors are rotating to match the direction of the magnetic field strength vector, reflected by rapid changes in the magnetization state. This causes a voltage to be induced in the measuring coil placed close to the surface of the examined material, which is called the Magnetic Barkhausen Noise. The MBN method makes it possible to detect stress [10; 11; 12; 13; 14], fatigue changes, and microstructural changes [15; 16] as well as study material properties [17; 18; 19; 20; 21]. The method enables the study of material property homogeneity and directionality, which enables the detection and analysis of the anisotropy of material properties [22; 23; 24; 25]. Anisotropy can be introducedbecause of exploitational changes in the material structure (e.g., directional stresses) or as a result of special manufacturing operations and the introduction of the desired characteristics of the material properties. Such properties are exemplary and possessed by specific types of electrical steel sheets. Generally, the two types can be distinguished: non-grain-oriented (NGO) and grain-oriented (GO) electrical steel [26]. NGO steels are widely used for the production of electric motors, generators and high-speed permanent magnet synchronous machines [27]. GO electrical steels are used in the production of transformers and non-rotating electrical devices [28], and these sheets have magnetic anisotropic properties. GO electrical sheets present as conventional (CGO) and with high magnetic permeability (HGO). In addition, these sheets can be subjected to various surface engineering methods that improve the magnetic parameters of the material through, for example, laser scribing. The MBN signal obtained from a material with such properties takes on the character of transitional phases (present in Figure 1) between successive Barkhausen effect activity stages occurring in the material and therefore requires advanced signal analysis methods sensitive enough to register those phases and enable quantitative assessment. Exemplary MBN signals registered for different magnetic directions (ascribed with different \(\alpha\) angles) of GO steel are presented in Figure 1. One can clearly see much higher activity in terms of Barkhausen jump amplitudes and their intensity sub-periods for an easy magnetic axis (\(\alpha=90^{\circ}\)) than for the other two. Currently, with the increase in computing capabilities of computers, signal analysis methods using time-frequency or time-scale representations are becoming more common [29; 30; 31; 32]. The use of time-frequency and time-scale representations to analyze the MBN signal has grown rapidly in recent years and has significantly enhanced the depth of understanding of the phenomena occurring in the process of reorientation of the magnetic domain structure during the magnetization process [23; 24; 25; 33; 34]. So far, various transformation methods have been used for MBN data, such as the time-scale wavelet or bilinear Wigner-Ville transform [35; 36; 34]. A detailed discussion on the transformation methods for MBN analysis was presented by the authors in [35; 34]. One of the frequently applied methods that stands out for its potential in the analysis of stochastic MBN signals is short-time Fourier Transform (STFI).