Editing in Freq domain and applying inverse FFT

In summary, the author has implemented a phase vocoder using an FFT routine and found that the results are smooth when windows are used.
  • #1
raminee
12
2
TL;DR Summary
How to overcome the distortions that result in editing in frequency domain before applying inverse FFT ?
Hello All,

I am somewhat familiar with FFT and iFFT and its uses.
However I have an issue when I edit in Freq domain and try to get back to time domain .

I have an audio signal in time domain that I transform to frequency domain using an FFT routine in block sizes of N points.
(in my case 256 samples)

I make some adjustments to the Real and Imaginary data based on some algorithm that I am working on.

I apply inverse FFT to get back to time domain.

I repeat this process for a number of blocks of N that forms my entire audio signal.

The resulted output audio signal has distortions mainly around the Block/Frame boundaries due to the changes that were made to the
real and imaginary samples in each Block.

How to remove block/frame boundary issues ?

Any suggestions would be helpful.

Thanks

Raminee
 
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  • #2
I have no expertise in this, but it is interesting that the MATLAB phase vocoder described here sums up the time domain results from the analysis done in the windows. I suspect that makes the transitions smooth.
 
  • #3
Thank you "FactChecker" for that info.
I have implemented the procedure as in the phase vocoder description and it works !!
THANKS AGAIN !!
Raminee
 
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  • #4
FactChecker said:
I have no expertise in this, but it is interesting that the MATLAB phase vocoder described here sums up the time domain results from the analysis done in the windows. I suspect that makes the transitions smooth.
I agree with this and the article linked to.

You can find out more about "windowing" at Window function and for this application the Hann function would be a suitable choice. Use blocks where each block is windowed by the Hann function and overlaps each of its neighbours by 50%.
 
  • #5
I used Hamming window with 50% overlap.
Any reasons Hann function would be better ?
 
  • #6
raminee said:
I used Hamming window with 50% overlap.
Any reasons Hann function would be better ?
Hann (a.k.a. Hanning) fades all the way to zero, whereas Hamming still has a small discontinuity. As the discontinuity is small, in practice you might not notice the difference. I'd be tempted to try both and see what difference it makes.
 
  • #7
For anyone interested I tried both windows and it is very very hard to tell the difference in the outputs.
At least with the audio signals I tried they both performed equally well under subjective testing.
Thx
 

What is the purpose of editing in the frequency domain?

The purpose of editing in the frequency domain is to manipulate the frequency components of a signal in order to achieve a desired result. This can include filtering out unwanted frequencies, enhancing certain frequencies, or combining multiple signals.

What is the process of editing in the frequency domain?

The process of editing in the frequency domain involves taking the Fourier transform of a signal to convert it from the time domain to the frequency domain. The signal can then be manipulated using mathematical operations such as filtering or scaling. Finally, the inverse Fourier transform is applied to convert the signal back to the time domain.

What is the benefit of using the inverse FFT?

The inverse Fast Fourier Transform (FFT) allows us to convert a signal back to the time domain after editing it in the frequency domain. This allows for more efficient and accurate manipulation of signals, as well as the ability to combine multiple signals in the frequency domain before converting them back to the time domain.

What types of signals can be edited in the frequency domain?

Any signal that can be represented in the time domain can also be represented in the frequency domain and therefore can be edited using this method. This includes audio signals, images, and even data from scientific experiments.

Are there any limitations to editing in the frequency domain?

One limitation of editing in the frequency domain is that it can be more complex and require more computational power compared to editing in the time domain. Additionally, the frequency components of a signal may not always correspond to the desired changes in the time domain, so careful consideration and understanding of the signal is necessary for effective editing.

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