<p>So if I understand correctly, you're trying to:</p>
<ol>
<li>Detect clicks in a three month long underwater recording made in the English Channel</li>
<li>Determine whether or not detected clicks are from marine mammals or other (echosounder, invertebrates, dumping of unsuitable product from <a href="https://en.wikipedia.org/wiki/Pop_Rocks" rel="nofollow noreferrer">Pop-Rocks</a> factory, or whatever)</li>
<li>For marine mammal clicks, you then want to try to identify down to lowest taxa (e.g. species where possible)</li>
</ol>
<p>Step 1 is straightforward and has a number of solutions, and it sounds like you don't have an issue with that.</p>
<p>Steps 2 and 3 are much harder -- especially if you are constrained to classifying individual clicks one-at-a-time (independently) without any additional context. One of the major problems with classification of individual clicks is that the characteristics of a detected click from an odontocetes are incredibly variable. Furthermore, much of this variability will arise from factors that are not related to the species (such as location and orientation of animal with respect to the hydrophone). Given the nature of this challenge, it might help to manage your expectations about how good the existing &quot;solutions&quot; to this problem actually are, and reconsider whether or not they will be useful as a &quot;reference&quot;.</p>
<p>A general approach that tends to be more successful than classifying individual clicks is to try to classify bouts/sequences/series of clicks that are proximate in time to each other and likely to have come from the same group of animals (sometimes called event). Check out <a href="https://onlinelibrary.wiley.com/doi/10.1111/mms.12381" rel="nofollow noreferrer">Rankin et al (2016)</a> for an detailed dive (no pun intended) into click classification vs event classification using ROCCA. In their own words, they suggest the performance of the ROCCA click classifier was &quot;mediocre&quot;, whereas their event-based classification algorithm <a href="https://github.com/EricArcher/banter" rel="nofollow noreferrer">BANTER (Bio-Acoustic eveNT classifiER)</a> performed much better. BANTER is open source, available as an R package, and has a detailed user guide and tutorial under development by one of the authors online at: <a href="https://taikisan21.github.io/PAMpal/banterGuide.html" rel="nofollow noreferrer">https://taikisan21.github.io/PAMpal/banterGuide.html</a>.</p>

CC BY-SA 4.0
2024-09-20T07:22:22.007Z
Very well, thank you for your help. I suspected that this operation would not necessarily be easy to perform. I'll look into BANTER to see what results I can get. 
Thanks again for your help!
Erwan
CC BY-SA 4.0
2024-09-18T04:13:26.647Z
2024-09-18T04:13:26.647Z
Brian Miller
<p>I would like to analyze a number of recording files containing, among other things, clicks of marine mammals. The background noise changes partly due to the strong tidal currents in the area.
I was able to carry out an initial analysis with python programs, but I want to compare these results with PamGuard software.
To this end, I'm using click analyses obtained with Click Detection and ROCCA for classification. Indeed, I've seen that ROCCA can analyze mammalian clicks. I use the ROCCA configuration files supplied by Pamguard (<a href="https://www.pamguard.org/downloadpage.php?id=81" rel="nofollow noreferrer">https://www.pamguard.org/downloadpage.php?id=81</a>) in the Eastern Atlantic region (which must correspond to my geographical area of the English Channel).
Rocca uses the results of mammal click pre-qualification and I'd like to know several things:</p>
<ul>
<li>firstly, whether my methodology was relevant to the classification of these detections (I'd at least like to know whether a click emitted could come from a dolphin, a porpoise, etc.). This approach only brings me results that are “Ambig” by ROCCA, which means that he normally can't make up his mind about classification. With other parameters, I could only find one dolphin species over the whole study period (3 months), so I think the results are wrong.</li>
<li>What if there were more efficient methods for carrying out this type of analysis?</li>
<li>If the simple identification of clicks by the clicks classification algorithm could provide sufficient results for an overview of mammal frequentation in the area and if certain parameters should be favored.</li>
</ul>
<p>Thank you in advance for your answers, I'm a novice in this discipline and I'm looking to learn a little more.</p>

CC BY-SA 4.0
2024-09-16T04:24:26.070Z
Hi and welcome to the community. It's not clear to me what is being asked here. The original post mentiond that you've run "analysis" in python and are seeking more efficient methods for "this type of analysis," but haven't specified what you're trying to achieve. "Analysis" in this context is ambiguous and could mean a lot of very different things to a lot of people. I suggest editing to provide more detail about what specifically you are trying to achieve, what specifically you've tried, and any other information about your dataset and situation that is relevant.
Brian Miller
CC BY-SA 4.0
2024-09-16T07:55:25.650Z
Indeed, I didn't go into enough detail. I'm looking to detect and classify clicks made by marine mammals on recordings. 
My analyses in python concern the detection and a first classification of mammal clicks by clusters, the algorithms coming mainly from this book: 
Zimmer WMX. Passive Acoustic Monitoring of Cetaceans. 2011
My aim here is to have “reference” results using Pamguard. This software has an efficient click detection algorithm, and I'd like to know what would be the most effective way of finding out whether a particular click is emitted by a particular species.
Erwan
<pamguard><echolocation-clicks><classification>
Basic clic classification using click detector and ROCCA in PamGuard
# Basic clic classification using click detector and ROCCA in PamGuard

(Asked by: username_0 on 2024-09-13)

I would like to analyze a number of recording files containing, among other things, clicks of marine mammals. The background noise changes partly due to the strong tidal currents in the area.
I was able to carry out an initial analysis with python programs, but I want to compare these results with PamGuard software.
To this end, I'm using click analyses obtained with Click Detection and ROCCA for classification. Indeed, I've seen that ROCCA can analyze mammalian clicks. I use the ROCCA configuration files supplied by Pamguard (<https://www.pamguard.org/downloadpage.php?id=81>) in the Eastern Atlantic region (which must correspond to my geographical area of the English Channel).
Rocca uses the results of mammal click pre-qualification and I'd like to know several things:

* firstly, whether my methodology was relevant to the classification of these detections (I'd at least like to know whether a click emitted could come from a dolphin, a porpoise, etc.). This approach only brings me results that are “Ambig” by ROCCA, which means that he normally can't make up his mind about classification. With other parameters, I could only find one dolphin species over the whole study period (3 months), so I think the results are wrong.
* What if there were more efficient methods for carrying out this type of analysis?
* If the simple identification of clicks by the clicks classification algorithm could provide sufficient results for an overview of mammal frequentation in the area and if certain parameters should be favored.

Thank you in advance for your answers, I'm a novice in this discipline and I'm looking to learn a little more.

**Tags:** pamguard, echolocation-clicks, classification

> **Comments:**
> **username_1**: Hi and welcome to the community. It's not clear to me what is being asked here. The original post mentiond that you've run "analysis" in python and are seeking more efficient methods for "this type of analysis," but haven't specified what you're trying to achieve. "Analysis" in this context is ambiguous and could mean a lot of very different things to a lot of people. I suggest editing to provide more detail about what specifically you are trying to achieve, what specifically you've tried, and any other information about your dataset and situation that is relevant.  
> **username_0**: Indeed, I didn't go into enough detail. I'm looking to detect and classify clicks made by marine mammals on recordings.  My analyses in python concern the detection and a first classification of mammal clicks by clusters, the algorithms coming mainly from this book:  Zimmer WMX. Passive Acoustic Monitoring of Cetaceans. 2011 My aim here is to have “reference” results using Pamguard. This software has an efficient click detection algorithm, and I'd like to know what would be the most effective way of finding out whether a particular click is emitted by a particular species.  

---

## 1 answers

### Answer by username_1 on 2024-09-18. Score: 1

So if I understand correctly, you're trying to:

1. Detect clicks in a three month long underwater recording made in the English Channel
2. Determine whether or not detected clicks are from marine mammals or other (echosounder, invertebrates, dumping of unsuitable product from [Pop-Rocks](https://en.wikipedia.org/wiki/Pop_Rocks) factory, or whatever)
3. For marine mammal clicks, you then want to try to identify down to lowest taxa (e.g. species where possible)

Step 1 is straightforward and has a number of solutions, and it sounds like you don't have an issue with that.

Steps 2 and 3 are much harder -- especially if you are constrained to classifying individual clicks one-at-a-time (independently) without any additional context. One of the major problems with classification of individual clicks is that the characteristics of a detected click from an odontocetes are incredibly variable. Furthermore, much of this variability will arise from factors that are not related to the species (such as location and orientation of animal with respect to the hydrophone). Given the nature of this challenge, it might help to manage your expectations about how good the existing "solutions" to this problem actually are, and reconsider whether or not they will be useful as a "reference".

A general approach that tends to be more successful than classifying individual clicks is to try to classify bouts/sequences/series of clicks that are proximate in time to each other and likely to have come from the same group of animals (sometimes called event). Check out [Rankin et al (2016)](https://onlinelibrary.wiley.com/doi/10.1111/mms.12381) for an detailed dive (no pun intended) into click classification vs event classification using ROCCA. In their own words, they suggest the performance of the ROCCA click classifier was "mediocre", whereas their event-based classification algorithm [BANTER (Bio-Acoustic eveNT classifiER)](https://github.com/EricArcher/banter) performed much better. BANTER is open source, available as an R package, and has a detailed user guide and tutorial under development by one of the authors online at: <https://taikisan21.github.io/PAMpal/banterGuide.html>.

> **Comments:**
> **username_0**: Very well, thank you for your help. I suspected that this operation would not necessarily be easy to perform. I'll look into BANTER to see what results I can get.  Thanks again for your help!  

---

