Wavelet Transform and STFT

1. What is the difference between STFT and wavelet transform?
2. What is the main advantage of wavelet analysis over STFT?
3. Why wavelet transform is better than short time Fourier transform?
4. What is the difference between FFT and STFT?

What is the difference between STFT and wavelet transform?

In contrast to the standard STFT which uses a single window size, the wavelet transform (WT) uses short windows at high frequencies and long windows at low frequencies [21]. Wavelets rely on the use of a mother wavelet function that can be scaled and shifted, to correlate with the anomalies or events of the signals.

What is the main advantage of wavelet analysis over STFT?

Wavelet analysis overcomes the disadvantage of STFT since CWT uses a windowing technique with variable sized regions. Wavelet analysis allows the use of long time intervals where we want more precise low-frequency information, and shorter regions where we want high-frequency information.

Why wavelet transform is better than short time Fourier transform?

The result of the wavelet transform differs from the STFT in that its time-frequency resolution is not fixed and depends on the frequency (multi-scale property, see Fig. 5). In general, the wavelet transform represents lower frequency components with finer frequency resolution and coarser time resolution.

What is the difference between FFT and STFT?

FFT has a resolution of 2048 lines, Blackman window, and 50% overlap and STFT also has Block size 2048, FFT size 16K, Blackman window used, and 50% overlap. As we can see, STFT performs better with the same block size (but more calculated lines). We improved frequency resolution for the same amount of scooped data.