## 5 Conclusions  
This article presents the results of work on the application of machine learning to the analysis of the time-frequency representation of Barkhausen's noise signals for the purpose of evaluating the angular properties of selected grain-oriented electrical steel sheets. The paper refers to two possible approaches based on a different form of representation of input signals, relating to the degree of their postprocessing. For the requirements of the first approach, in the previous work [26], the authors presented the results of a procedure for analyzing the basic representation of MBN signals (raw signals transformed into time-frequency domain) based on deep learning. This paper presents a second type: a classic approach based on the extraction of unambiguously definable features of signals, and the search for intricate rules and relationships between them. For this purpose, a group of classic machine learning algorithms with varying degrees of model complexity and computational requirements was considered. Ensembled classifiers (Bagged Trees along with the Subspace Discriminant) showed relatively good behavior in reference to the others in each analyzed case. This showed a potential in this classifier type. However, it must be underlined that the ANN and SVM models performed better in the case of a smaller number of labelled classes, but the difference was not significant. Finally, both approaches, referring to the classical ML models and the DL one, were compared. The obtained results make it possible to compare the accuracy of both approaches to MBN analysis by carrying out research on a common database. Such a comparison was not the subject of any previous work and thus is a value added to future work on this topic.  
Presented in this article, the comparison of machine learning methods (based on the parameter vector) with deep learning (based on the full representation of measurement signals) shows the superiority of the latter approach in the context of the achieved accuracy. In the case of machine learning, the results in accuracy were 10.1% to 13.9% worse than those obtained from deep learning methods. This leads to the need to pay special attention to the deep learning methods that were presented in the previous paper [26]. Nevertheless, it seems that considering the high availability of computer units with relatively high computing capabilities, the possibility of building systems based on deep learning seems to have significant potential for use in measurement data analysis systems, especially of a stochastic nature, such as magnetic Barkhausen noise.  
**Author Contributions:** Conceptualization, M.M. and G.P.; methodology, M.M. and G.P.; software, M.M.; validation, M.M.; investigation, M.M. and G.P.; resources, G.P.; data curation, M.M.; writing--original draft preparation, M.M.; writing--review and editing, M.M. and G.P.; visualization, M.M.; supervision, G.P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The work was funded by Research Funds of Faculty of Electrical Engineering of West-Pomeranian University of Technology, Szczecin.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors would like to express special thanks to [PERSON] from the West Pomeranian University of Technology in Szczecin in particular for valuable discussions and cooperation in the realization of metallographic research.  
**Conflicts of Interest:** The authors declare no conflict of interest.