Short-time wavelet transform

1. What is meant by short-time Fourier transform?
2. What is the difference between FFT and STFT?
3. What is the main advantage of wavelet analysis over STFT?
4. How is integral wavelet transform different from STFT?

What is meant by short-time Fourier transform?

The short-time Fourier transform (STFT) is used to analyze how the frequency content of a nonstationary signal changes over time. The magnitude squared of the STFT is known as the spectrogram time-frequency representation of the signal.

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.

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.

How is integral wavelet transform different from STFT?

The wavelet transforms presents an improvement over the STFT because it has great resolutions in both time and frequency domain. Wavelet transform represents signals with energy-concentrated basic functions instead of invariable sized window functions which used in STFT.