1
$\begingroup$

I am keen to know that If I have two signals say :

Farend_signal (y[n]) and Nearend_signal (x[n])

and x[n] also contains an echo of y[n] with some delay.

Is there any technique to align these two signals temporally is Spectrogram? For example, any correlation technique to find the temporal displacement between signals after making a spectrogram(STFT) of two signals?

Improve this question
$\endgroup$
8
  • 1
    $\begingroup$ Techniques you already know in the time domain for template matching, adapted filtering, echo detection, etc. can be transported in a time-scale or time-frequency domain, where the processing is easier due to better signal separation $\endgroup$
    – Laurent Duval
    Nov 28, 2020 at 11:07
  • 1
    $\begingroup$ In addition to what Laurent said: note that the spectrogram is usually a power thing and thus "erases" phase information; you can't measure fine temporal shifts of signals in a power description of frequency domain. So, I'll go with: using the spectrogram is not a good method. $\endgroup$
    – Marcus Müller
    Nov 28, 2020 at 13:06
  • $\begingroup$ @LaurentDuval Thank you so much for the clarification. $\endgroup$
    – Khubaib Ahmad
    Nov 30, 2020 at 9:32
  • $\begingroup$ @MarcusMüller Okay I got it. Actually, I was following this question [Eliminate Signal A from Signal B] (dsp.stackexchange.com/questions/7593/…). But I wasn't able to eliminate the signal completely. So, after analysis, I thought that there might be some temporal displacement between the signals so that's why I thought of this technique. $\endgroup$
    – Khubaib Ahmad
    Nov 30, 2020 at 9:34
  • 1
    $\begingroup$ I agree with @Marcus. I was just supposing you meant a short-time Fourier transform $\endgroup$
    – Laurent Duval
    Nov 30, 2020 at 9:37

1 Answer 1

Reset to default
1
$\begingroup$

You can find a large number of "model-based echo suppression" or "adaptive acoustic echo cancellation" (for the keywords). They often combine a sparsifying transformation (wavelets, time-frequency decompositions) and some form of fitting or adaptation of the model to the time and frequency localization where the echo dwells. Underlying hypotheszs are:

  • the model somehow looks like the echo,
  • the similarity will increase in the transformed domain,
  • this representation will help the morphing, or make is more efficient (faster, etc.)

For voice, you find a look of literature, overviews, etc. You may also look at neighboring domains: vibration-based non-destructive testing or monitoring, ultrasounds. I would mention seimic processing, where echoes are sometimes named multiples (waves bounding several times between layers). Here are two examples I have been involved in. A first one quite fast which uses 1-tap adaptive filters (hint: a complex filter), the second more involved with optimization constraints. They use time-scale wavelets, but could be readily adapted to short-term Fourier transforms or multi-input/multi-output filter banks.

Template-based echo suppression in seismic data

Improve this answer
$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge that you have read and understand our privacy policy and code of conduct.

Not the answer you're looking for? Browse other questions tagged or ask your own question.