filtering difference b/w freq and time domain When you want to get the specific frequency (ex. around 1000Hz), you might

take FFT, remain the frequency you want, and reduce the power of the other frequencies you do not.
ex.) if fs=16khz and fftsize=1024, remain the power of bin64 and reduce all other bins
design band pass filtering (Butterworth or others) to pass 1000Hz (with stop/transition band also)

In short, 1 is in frequency domain, and 2 is in time domain.
Then my question is: are these two methods same?
(I think yes in terms of &quot;extracting 1000Hz signal&quot;(Both can do))
If yes, how would you utilize these two methods?
If not, how different are these (and how would you utilize)?
I would appreciate your answers. Thank you.
Multiplication in the frequency domain is the equivalent of convolution (digital filtering) in the time domain - one of the fundamental properties of the Fourier transform.
Using an FFT in practice to filter requires stitching together overlapping chunks and a large delay if in real-time.  However in the frequency domain you can immplement brick-wall filters as easy as any, which is much more costly in time-domain.
The algorithms for fft convolution include &quot;overlap-add&quot; and &quot;overlap-save&quot;, these are useful search terms on the matter.
The Python scipy.signal library has an implementation of convolve that automatically selects direct filtering or fft convolution based on an estimate of which will be faster.