###### Abstract
One of the main advantage of WVD transformation is that in the case of non-stationary signals, it enables one to achieve high resolution both over time and frequency [9]. The above-mentioned transformations have found many applications in signal analysis so far. Some of them are given below. In [10], several TF transforms were compared, including the STFT, the WVD and the SPWVD as well. The TF transforms were used to study seismocardiographic signals. A comparison was achieved by building two synthetic signals with known TF distributions resembling the real ones: first with time-independent frequencies and the second one with varying frequency components. Then, by using the above-mentioned transformations, the detection of appropriate instantaneous frequency (IF) was made. For the first signal type, the lowest Normalized Root Mean Square Error (NRMSE) value was obtained for the STFT transformation and for the second one, the lowest value was found for the SPWVD. The STFT brought no significant advantage over the SPWVD, except for simplicity. Simultaneously, the performance of the WVD was assessed to be the made worse (as it appeared to have, i.e., the highest artifacts). Similarly, in [11], various TF transformations were used to analyze EEG signals. The STFT transformation was also used to study the low-frequency signals generated by wind turbines [12]. In [13; 14], the FSST was used to analyze seismological signals. In paper [14], the authors showed that in this application of the FSST can be used to detect low-frequency shadow anomalies (these are frequency bands related to hydrocarbons) and to obtain more favorable results than in relation to the STFT. In article [15], the WSST was used to analyze signals obtained by an ultrasound transducer and laser vibrometer. In this application, the WSST improved the sensitivity to damage detection by 36%. The WSST was also used to carry out research on the instantaneous resonance frequency of the modelled bridge over which the mass was moving [16]. It has been shown that the WSST can be used to monitor the condition of the bridge and detect the structure damage. Authors proposed that the WSST could be used monitor the health of structure in quasi real time. Additionally, until now, several studies have also been published on the application of the TF transforms to the MBN signal analysis. The aggregate qualitative comparison of the TF transformation, such as the STFT and the WVD, was published in [17]. An extended analysis of the TF transformation for steels that have been hardened was carried out in [18]. The paper presents various transformations, such as the STFT, the SPWVD and the CWT as well. Various display modes were used to present qualitative and quantitative analysis. The study has shown that signal analysis through the TF and TS representations can provide valuable information about the MBN phenomenon course under the influence of various material properties. In [19], the possibility of the parametrization of the STFT spectrogram was presented in order to study the stress level in structural steel. In [20], the authors investigated the TFR of the MBN signals for magnetic anisotropy evaluation in a grain-oriented electrical steel sheet. The possibility of dividing the spectrogram into temporal subperiods, that correspond to a particular subprocess of the reorganization of domain structure, has been presented. The analysis provides information on changes in MBN activity in specific time periods of the magnetization process for different directions of the magnetic field, which may be helpful in explaining the source of the resultant anisotropy characteristic in ferromagnetic materials. In paper [21], the determination of easy and hard magnetization axes were presented by analyzing the MBN signals using the STFT transform. The influence of the measuring condition on the quality of information on magnetic anisotropy was also analyzed by the authors. The wavelets were applied successfully as a tool to analyze the MBN signal for eq.