<p>A labmate of mine has had good luck with long-term false color spectrograms, following the protocol of <a href="https://www.researchgate.net/publication/324796087_Long-duration_false-colour_spectrograms_for_detecting_species_in_large_audio_data-sets" rel="nofollow noreferrer">this paper</a> from Michael Towsey et al.</p>
<p>I've also done a fair bit of simply showing loess-smoothed time series of various acoustic indices, using the geom_smooth() function from ggplot() in R. It can take a bit of fussing to find a span to apply to the smoother to get it looking good but it can be pretty useful.</p>

CC BY-SA 4.0
2022-07-25T21:29:25.130Z
2022-07-25T21:29:25.130Z
dtsavage
<p>I'd also recommend another paper from the QUT group that @dtsavage mentions: Phillips et al (2018) &quot;Revealing the ecological content of long- duration audio-recordings of the environment through clustering and visualisation&quot; PLoS ONE <a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0193345" rel="nofollow noreferrer">https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0193345</a></p>
<p>They show a range of interesting options for visualising long-duration recordings.</p>
<p>I really like long-duration false-colour spectrograms, especially for data exploration, but unfortunately they may not always be colour-blind friendly. Most rely on 3 channel Red-Green-Blue combinations to depict the spectro-temporal differences. You might need to create custom functions to force use of a colour-blind friendly palette, especially if you are hoping to directly compare spectrograms from different sources. The colours are assigned through relative rather than absolute scores of the 3 indices. So two e.g. red areas in a pair of spectrograms may not necessarily reflect the same soundscape patterns.</p>

CC BY-SA 4.0
2022-07-26T07:42:44.833Z
2022-07-26T07:42:44.833Z
EcologyTom
<p>As the OP indicates there are two aspects</p>
<ul>
<li>compression of long data sets into a smaller dataset that is easier to analyse</li>
<li>'color-scheme' that support analysis and are suited for color blindness.</li>
</ul>
<p>Adapting from spectrogram, one could have time on x-axis, and relevant  categories (may be in the order of frequency) on y-axis
For The compression, you must decide if the parameter can be smoothed and you are only interested in mean value (background noise, wind, rain, ships, etc) than you smooth/average, or  you are interested in occurrence of events (calls, clicks, etc.) then you keep always the maximal value.</p>
<p>Concerning color and color-blindness, it still seems appropriate to use gray-scale or at least diverging colomaps that work both on screen and on paper.</p>

CC BY-SA 4.0
2022-07-26T07:01:58.180Z
2022-07-26T07:01:58.180Z
WMXZ
<p>I'm interested in effective methods to visualize and subsequently analyze long-term acoustic datasets, including those from disparate datasets. Are there preferred color schemes that work well for color blindness and intuitive interpretation?</p>

<analysis><soundscape>
What soundscape visualizations are useful for assessing biodiversity over long time periods (>1 year)
# What soundscape visualizations are useful for assessing biodiversity over long time periods (>1 year)

(Asked by: username_0 on 2022-07-25)

I'm interested in effective methods to visualize and subsequently analyze long-term acoustic datasets, including those from disparate datasets. Are there preferred color schemes that work well for color blindness and intuitive interpretation?

**Tags:** analysis, soundscape


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## 3 answers

### Answer by username_1 on 2022-07-25. Score: 4

A labmate of mine has had good luck with long-term false color spectrograms, following the protocol of [this paper](https://www.researchgate.net/publication/324796087_Long-duration_false-colour_spectrograms_for_detecting_species_in_large_audio_data-sets) from Michael Towsey et al.

I've also done a fair bit of simply showing loess-smoothed time series of various acoustic indices, using the geom\_smooth() function from ggplot() in R. It can take a bit of fussing to find a span to apply to the smoother to get it looking good but it can be pretty useful.


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### Answer by username_2 on 2022-07-26. Score: 4

I'd also recommend another paper from the QUT group that @username_1 mentions: Phillips et al (2018) "Revealing the ecological content of long- duration audio-recordings of the environment through clustering and visualisation" PLoS ONE <https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0193345>

They show a range of interesting options for visualising long-duration recordings.

I really like long-duration false-colour spectrograms, especially for data exploration, but unfortunately they may not always be colour-blind friendly. Most rely on 3 channel Red-Green-Blue combinations to depict the spectro-temporal differences. You might need to create custom functions to force use of a colour-blind friendly palette, especially if you are hoping to directly compare spectrograms from different sources. The colours are assigned through relative rather than absolute scores of the 3 indices. So two e.g. red areas in a pair of spectrograms may not necessarily reflect the same soundscape patterns.


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### Answer by username_3 on 2022-07-26. Score: 3

As the OP indicates there are two aspects

* compression of long data sets into a smaller dataset that is easier to analyse
* 'color-scheme' that support analysis and are suited for color blindness.

Adapting from spectrogram, one could have time on x-axis, and relevant categories (may be in the order of frequency) on y-axis
For The compression, you must decide if the parameter can be smoothed and you are only interested in mean value (background noise, wind, rain, ships, etc) than you smooth/average, or you are interested in occurrence of events (calls, clicks, etc.) then you keep always the maximal value.

Concerning color and color-blindness, it still seems appropriate to use gray-scale or at least diverging colomaps that work both on screen and on paper.


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