Comparison of methods for signal analysis in the time-frequency domain

This paper shows the most relevant results of the comparison of four signal analysis methods in the time-frequency domain: Short Time Fourier Transform, Wigner-Ville Distribution, Wavelets and Matching Pursuit, using an artificially created signal. This was done in order to look for the advantages and disadvantages of each of these methods in terms of frequency resolution, time resolution, detection and computational load. For the comparison, five experiments were performed with the artificial signal. Each new test demands more strict conditions for time resolution, frequency resolution and component detection due to the amplitude reduces and frequency separation decreases among components. The results show that, the best method in terms of frequency resolution, detection and computational load is the Short Time Fourier Transform. On the other hand, Bump Wavelet, which is also the best among the wavelets analyzed, has the best time resolution allowing to distinguish the start and end times of each component of the signal with excellent precision for each of the tests performed.