<p>While I don't use Raven, here is an example of a revealing way to show long-term soundscapes. Ref is this paper:</p>
<blockquote>
<p>Ladegaard, M., Macaulay, J., Simon, M. et al. Soundscape and ambient
noise levels of the Arctic waters around Greenland. Sci Rep 11, 23360
(2021). <a href="https://www.nature.com/articles/s41598-021-02255-6" rel="nofollow noreferrer">https://doi.org/10.1038/s41598-021-02255-6</a></p>
</blockquote>
<p>Here, we see see plots where the location around the circle shows the time, and the distance from the center to the outside shows the frequency. Noise levels and detections of clicks, whistles, moans and seismic surveys are shown. Perhaps both analyzers could make similar plots and then compare those with one another?</p>
<p><a href="https://i.sstatic.net/up00Z.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/up00Z.png" alt="Figure from Ladegaard et al., 2021" /></a></p>

CC BY-SA 4.0
2022-08-17T08:55:03.923Z
2022-08-17T08:55:03.923Z
Chloe
<p>I am looking for a tool to visualize detections (species of choice) in long-term recordings (e.g. over month, over year). I have manual annotations of many files produced in Raven Pro. Raven selection tables include filename, date, clock time and offset time from the beginning of the file.</p>
<p>Ideally, it would be possible to select the range (hour, day, week, month, year) and detail of visualization (e.g. day, hour, minute). Ideally, one could visualize different annotations (two people, ground truth X automatic detection) for same files side-by-side for a quick comparison of detection methods.</p>
<p>I know RavenPro can visualize multiple selection tables at the same time. But this is not suitable when presenting long-term data. I know this should be possible in scikit-maad (<a href="https://scikit-maad.github.io/util.html" rel="noreferrer">https://scikit-maad.github.io/util.html</a>) but, probably not as ready to use function. I know sed_vis Python tool box (<a href="https://github.com/TUT-ARG/sed_vis" rel="noreferrer">https://github.com/TUT-ARG/sed_vis</a>) is made for similar task but it also does not seem to be aimed at long-term visualizations.</p>

<automated-detection><annotations><raven>
Tools to visualize and compare species detections in long-term recordings
# Tools to visualize and compare species detections in long-term recordings

(Asked by: username_0 on 2022-08-09)

I am looking for a tool to visualize detections (species of choice) in long-term recordings (e.g. over month, over year). I have manual annotations of many files produced in Raven Pro. Raven selection tables include filename, date, clock time and offset time from the beginning of the file.

Ideally, it would be possible to select the range (hour, day, week, month, year) and detail of visualization (e.g. day, hour, minute). Ideally, one could visualize different annotations (two people, ground truth X automatic detection) for same files side-by-side for a quick comparison of detection methods.

I know RavenPro can visualize multiple selection tables at the same time. But this is not suitable when presenting long-term data. I know this should be possible in scikit-maad (<https://scikit-maad.github.io/util.html>) but, probably not as ready to use function. I know sed\_vis Python tool box (<https://github.com/TUT-ARG/sed_vis>) is made for similar task but it also does not seem to be aimed at long-term visualizations.

**Tags:** automated-detection, annotations, raven


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## 1 answers

### Answer by username_1 on 2022-08-17. Score: 4

While I don't use Raven, here is an example of a revealing way to show long-term soundscapes. Ref is this paper:

> Ladegaard, M., Macaulay, J., Simon, M. et al. Soundscape and ambient
> noise levels of the Arctic waters around Greenland. Sci Rep 11, 23360
> (2021). [https://doi.org/10.1038/s41598-021-02255-6](https://www.nature.com/articles/s41598-021-02255-6)

Here, we see see plots where the location around the circle shows the time, and the distance from the center to the outside shows the frequency. Noise levels and detections of clicks, whistles, moans and seismic surveys are shown. Perhaps both analyzers could make similar plots and then compare those with one another?

[![Figure from Ladegaard et al., 2021](https://i.sstatic.net/up00Z.png)](https://i.sstatic.net/up00Z.png)


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