17
$\begingroup$

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?

$\endgroup$

3 Answers 3

4
$\begingroup$

A labmate of mine has had good luck with long-term false color spectrograms, following the protocol of this paper 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.

$\endgroup$
4
$\begingroup$

I'd also recommend another paper from the QUT group that @dtsavage 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.

$\endgroup$
3
$\begingroup$

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.

$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.