Howtosignalprocessing
- What is the limitation of STFT in multi resolution analysis of signals?
- What is time and frequency resolution?
- How can you get a high frequency resolution in the spectrum of the FFT?
- What are the advantages of STFT over a simple FFT?
What is the limitation of STFT in multi resolution analysis of signals?
One of the pitfalls of the STFT is that it has a fixed resolution. The width of the windowing function relates to how the signal is represented—it determines whether there is good frequency resolution (frequency components close together can be separated) or good time resolution (the time at which frequencies change).
What is time and frequency resolution?
A consequence of Fourier transforms being built from is that scaling a function to be narrower in one domain scales it to be wider in the other domain. Scaling implies inverse scaling of t to keep the product constant. For example, the FT of a rectangle is a sinc.
How can you get a high frequency resolution in the spectrum of the FFT?
The most intuitive way to increase the frequency resolution of an FFT is to increase the size while keeping the sampling frequency constant. Doing this will increase the number of frequency bins that are created, decreasing the frequency difference between each.
What are the advantages of STFT over a simple FFT?
If you look closely, there is a difference in the time frame on 3D graphs between STFT and FFT. STFT has smaller time frames, consequently, the frequency spectrum moves smoother over time, therefore it is more accurate. Block size - defines the number of real data samples to be taken for the calculating FFT.
