Short-time Fourier Transform and the Spectogram

Great video! Nice explanation for window the data, comprehension about formula that is used and it is just so easy to understand your point of view. Congrats, you are a good professor.

This was very helpful, thank you!

Good Explanation Thank you!

Great explanation thank you

WOW now I see it clearer! One question just to know, in the formula could it be x[n-m] instead of x[n+m] to advance the signal x forward? thank u.

Can we get information for the phase of specific frequency? Thanks!

This has been implemented in Matlab as spectrogram() function.

How do we determine the optimal window size?
How does overlap affect the frequency-time resolution?
Thank you.

fs = 44.1 kHz

great!
thanks :)

Great Explanation, thanks!

excellent

amazing!!!!! thank you

Arent you the man who co-authored Simon Haykin's signal and systems book ?

very good. Thank you for that

Wonderful lecture! Thank you very much Sir.

what is the different n and m? may i have your email, sir?

I am looking for a relation between length of a window and bandwidth of the filter. Any clues/hints?

Can we "hack" the "no free lunch here" problem by getting lets say 128 samples and pad them with zeros up to 2048 and then take FFT? Wouldn't it give us bigger resolution and better "dynamics"?

wow

thank you so much!

Hi Mr. Van Veen
could you please help me with this question?
Develop a sliding DFT algorithm and compute sliding DFT for x(n) = [0,1,2,3,4,5]. if sliding window length-4.

what is the different between L and N in the saxophone riff section? doesnt N suppose to have the same number as sampling rate?

Even though I am a wavelet guy, I can still appreciate short time fourier transform.

Excellent Explanation. Thanks from Colombia