Answers.Body: <p>Theres also the BioCPP neural network (<a href="https://www.nature.com/articles/s41598-021-02790-2" rel="nofollow noreferrer">paper link</a>) that may be of relevance.</p>
  <p>Bermant 2021 demonstrates source separation of overlapping sounds for groups of up to 3 individuals. However, if I understood correctly, the network separates individuals in mixed audio that is was already trained to - which may/not be useful if the recorded audio has 'unfamiliar' individual calls from the field.</p>
  <p>As mentioned by Dan Stowell the presence od multiple mics allows particular types of source separation methods to be applied e.g. DUET and others ( see <a href="https://onlinelibrary.wiley.com/doi/pdf/10.1002/ima.20035?casa_token=GLykRSwzoxsAAAAA:VNimNV_ZIBeXs9P2vP0dLAfvJthadY9UQXJto_-vcWqoR65VrBf7-MsMZj3lWBWsGxOSfXvcjPh2VqDh5w" rel="nofollow noreferrer">here</a> for a review). Many of these algorithms often already have publicly available software implementations  (e.g. <a href="https://github.com/nussl/nussl" rel="nofollow noreferrer"><code>nussl</code></a> or <a href="https://librosa.org/doc/main/advanced.html" rel="nofollow noreferrer"><code>librosa</code></a>)written for music/speech separation. It may help to experiment with these implementations and changing the default parameters.</p>
  
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Answers.UserDisplayName: Thejasvi
Answers.Body: <p>Caveat: I have no solution, but there are some ideas I would try.</p>
  <p>FM signals are very often matched-filtered (effectively time compressed to a single pulse) allowing easier observation of the multi-pulse arrival structure.</p>
  <p>I assume that there are no replicas available, also AFAIK, bat FM sweeps are varying in time as they approach the prey/object.</p>
  <p>So, what I would try is to cross-correlate all the microphone measurement. As the different bat calls arrive at different times on the hydrophones, they could separate out, and the individual calls could then be time-aligned averaged. You may have lost the time details of your calls, but the power spectrum should be maintained. Effectively, you do signal description after tracking.</p>
  
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Answers.Body: <p>Here's a paper I'm lead author on where we did just that, but the credit goes all to my former PhD student (first author): <a href="https://asa.scitation.org/doi/full/10.1121/10.0000932" rel="nofollow noreferrer">Izadi et al., 2020. Separation of overlapping sources in bioacoustic mixtures, JASA.</a></p>
  <p>This approach uses a supervised learning framework and isolates calls in the time frequency domain. First, it detects the number of sources and then it extracts the signals in the time-frequency domain. By using deep neural networks, similar to bird audio detection, it can handle signals that overlap in time and/or frequency.</p>
  <p>The code for this is posted on github: <a href="https://github.com/russellizadi/echolocation_separation" rel="nofollow noreferrer">https://github.com/russellizadi/echolocation_separation</a></p>
  
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Answers.Text: Looks like a good answer (though it's just 1 method). I'd like to point out that there's a lot of work in the speech and music domains on source separation, and some of it should(?) be appropriate in bioacoustics too, though it needs evaluating. For example, just today I find this new paper on "Distance-Based Sound Separation" whose core idea might be relevant https://arxiv.org/abs/2207.00562
Answers.UserDisplayName: Dan Stowell
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Answers.Text: @Dan Stowell: would be great to see this as a separate (more fleshed out?) answer in itself :)!!
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Answers.UserDisplayName: Laura Kloepper
Body: <h3>Background</h3>
  <p>I'm currently handling a multi-microphone dataset with groups of free flying frequency-modulating (FM) bats (1-30 bats). I'd like to assess if bats change their call bandwidth across group sizes.</p>
  <p>Studies so far have either a) looked for non-overlapping calls [1] or b) performed 'aggregated' measurements [2]. Both are problematic for my particular dataset, as with larger groups it is difficult to find non-overlapping calls and I'd like to try to get individual call data (i fear that 'aggregated' measurements will be dominated by calls closest to the nice).</p>
  <h3>Problem statement</h3>
  <p>I'd like to extract individual call data even though it may be overlapped, and then perform spectral measurements (peak frequency, bandwidth etc). Are there existing methods out there developed for bat or other animal calls?</p>
  <h4>Refs</h4>
  <ol>
  <li>Gillam et al. 2010, J. Mammology</li>
  <li>Lin et al. 2016, JASA</li>
  </ol>
  
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Comments.Text: Could you please specify whether you have any possibility of spatial separation of sounds (e.g. multi-microphone), or not? This will make a big difference to what is the right answer, in this particular case.
Comments.UserDisplayName: Dan Stowell
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Comments.CreationDate: 2022-06-28T12:28:51.647Z
Comments.Text: done. Changed 'multi-channel' to multi-mic to clarify.
Comments.UserDisplayName: Thejasvi
Tags: <measurement>
Title: Methods to analyse overlapping calls
ThreadText: # Methods to analyse overlapping calls
  
  (Asked by: username_0 on 2022-06-28)
  
  ### Background
  
  I'm currently handling a multi-microphone dataset with groups of free flying frequency-modulating (FM) bats (1-30 bats). I'd like to assess if bats change their call bandwidth across group sizes.
  
  Studies so far have either a) looked for non-overlapping calls [1] or b) performed 'aggregated' measurements [2]. Both are problematic for my particular dataset, as with larger groups it is difficult to find non-overlapping calls and I'd like to try to get individual call data (i fear that 'aggregated' measurements will be dominated by calls closest to the nice).
  
  ### Problem statement
  
  I'd like to extract individual call data even though it may be overlapped, and then perform spectral measurements (peak frequency, bandwidth etc). Are there existing methods out there developed for bat or other animal calls?
  
  #### Refs
  
  1. Gillam et al. 2010, J. Mammology
  2. Lin et al. 2016, JASA
  
  **Tags:** measurement
  
  > **Comments:**
  > **username_1**: Could you please specify whether you have any possibility of spatial separation of sounds (e.g. multi-microphone), or not? This will make a big difference to what is the right answer, in this particular case.  
  > **username_0**: done. Changed 'multi-channel' to multi-mic to clarify.  
  
  ---
  
  ## 3 answers
  
  ### Answer by username_3 on 2022-06-28. Score: 4 | Accepted answer
  
  Here's a paper I'm lead author on where we did just that, but the credit goes all to my former PhD student (first author): [Izadi et al., 2020. Separation of overlapping sources in bioacoustic mixtures, JASA.](https://asa.scitation.org/doi/full/10.1121/10.0000932)
  
  This approach uses a supervised learning framework and isolates calls in the time frequency domain. First, it detects the number of sources and then it extracts the signals in the time-frequency domain. By using deep neural networks, similar to bird audio detection, it can handle signals that overlap in time and/or frequency.
  
  The code for this is posted on github: <https://github.com/russellizadi/echolocation_separation>
  
  > **Comments:**
  > **username_1**: Looks like a good answer (though it's just 1 method). I'd like to point out that there's a lot of work in the speech and music domains on source separation, and some of it should(?) be appropriate in bioacoustics too, though it needs evaluating. For example, just today I find this new paper on "Distance-Based Sound Separation" whose core idea might be relevant https://arxiv.org/abs/2207.00562  
  > **username_0**: @username_1: would be great to see this as a separate (more fleshed out?) answer in itself :)!!  
  
  ---
  
  ### Answer by username_0 on 2022-06-29. Score: 1
  
  Theres also the BioCPP neural network ([paper link](https://www.nature.com/articles/s41598-021-02790-2)) that may be of relevance.
  
  Bermant 2021 demonstrates source separation of overlapping sounds for groups of up to 3 individuals. However, if I understood correctly, the network separates individuals in mixed audio that is was already trained to - which may/not be useful if the recorded audio has 'unfamiliar' individual calls from the field.
  
  As mentioned by username_1 the presence od multiple mics allows particular types of source separation methods to be applied e.g. DUET and others ( see [here](https://onlinelibrary.wiley.com/doi/pdf/10.1002/ima.20035?casa_token=GLykRSwzoxsAAAAA:VNimNV_ZIBeXs9P2vP0dLAfvJthadY9UQXJto_-vcWqoR65VrBf7-MsMZj3lWBWsGxOSfXvcjPh2VqDh5w) for a review). Many of these algorithms often already have publicly available software implementations (e.g. [`nussl`](https://github.com/nussl/nussl) or [`librosa`](https://librosa.org/doc/main/advanced.html))written for music/speech separation. It may help to experiment with these implementations and changing the default parameters.
  
  
  ---
  
  ### Answer by username_2 on 2022-06-28. Score: 1
  
  Caveat: I have no solution, but there are some ideas I would try.
  
  FM signals are very often matched-filtered (effectively time compressed to a single pulse) allowing easier observation of the multi-pulse arrival structure.
  
  I assume that there are no replicas available, also AFAIK, bat FM sweeps are varying in time as they approach the prey/object.
  
  So, what I would try is to cross-correlate all the microphone measurement. As the different bat calls arrive at different times on the hydrophones, they could separate out, and the individual calls could then be time-aligned averaged. You may have lost the time details of your calls, but the power spectrum should be maintained. Effectively, you do signal description after tracking.
  
  
  ---
  
  