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This is a follow up question to: Segment/Idenfication of signal from a spectrogram

My main goal is to identify (segment) all of the calls from a given signal. The signal looks like the following (Spectrogram): enter image description here

Notice how there are 4 calls within this signal, I would like to identify there are 4 calls and just capture these calls so I can then use this for later analysis.

So far I have carried out the following:

  • Computed an STFT (Spectrogram on the time-signal) of a series of bat calls.

The spectrogram shows where each of the calls are, by the high energy levels.

What I am now calculating is the time variance between all of the different calls using the following formula:

$$ T = \sum_a^b x \sqrt{re*re+im*im} $$

This is calculated for each of the bins, the result is as follows:

enter image description here

Where I belive the x axis relates to the time, and the y axis is the total variance

From this, it is clear where the most significant parts of the signal are, the 4 spikes indicate the parts that I need to extract, it's just how... I suppose I could use a threshold value, and state that:

if variance > THRESHOLD:
   keep the block 
else:
   place "0" in each of the elements 

But, how would I calculate the threshold in order to the above? What if the next set of calls are different to the threshold set for this?

Any help would be appreciated.

EDIT:

By doing the following (above) and, using a threshold value, I get the following result:

enter image description here

Therefore, can I calculate each of the frequencies (of the bird calls) based upon this? I.e. $$(binnumber * Fs) / NFFT$$

Where Fs is the frequency sample rate and NFFT is the size of each of the bins?

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  • $\begingroup$ @down-voter - Why has this question been downvoted? :s $\endgroup$ – Phorce Jan 30 '14 at 0:38
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You could use a factor $0<c<1$ of the average of the variances

$\mathrm{THRESHOLD}=c\cdot{\operatorname{avg}(\operatorname{var}(X))}$

Since the ,,calls'' (I suppose you're referring to birds) start sharply but fade out gradually, you could extend every period a little in the front and a little more in the end.

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  • $\begingroup$ Hello, please see my updated post! =) thanks for the reply, though! It worked, kind of! $\endgroup$ – Phorce Jan 29 '14 at 19:36
  • $\begingroup$ I would have expected that you use a lower c to cover more of the signal. But anyways, you should be able to find the peak value. The spectrogram however shows rather broadband signals, so I would not expect the result to be very clear. $\endgroup$ – user7358 Jan 29 '14 at 19:58
  • $\begingroup$ Sorry, what do you mean by lower c what would the c value be in this case? Either way, the values seem pretty clear. On every signal I have tested with; the algorithm is working fine.. Just need to calculate the correct threshold value now $\endgroup$ – Phorce Jan 29 '14 at 20:02
  • $\begingroup$ @user1326876 - Also, is it possible to plot the frequencies related to the bins? (the way I described in my edit) $\endgroup$ – Phorce Jan 29 '14 at 20:05
  • $\begingroup$ I just noticed I was talking about $\mathrm{THRESHOLD}=(1-c)\cdot\mathrm{min}(\mathrm{var}(X))+c\cdot \mathrm{max}(\mathrm{var}(X))$. Either way, $c$ is just a weight. Bin index is proportional to frequency, yes. $\endgroup$ – user7358 Jan 29 '14 at 20:10

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