Hi Tullio,

I've started writing my own functions in R for analyzing sound using the following package:

http://rug.mnhn.fr/seewave/

I'm not sure how long/large of a file it can handle, however, so you might want to break your soundscape files into short segments, so that your analysis is also segmented. For processing, I've started using Sound Exchange (command line):

http://sox.sourceforge.net/

I hope these resources help!

Dear Tullio,

There is no magic bullet that will let you detect every type of signal.  For general data exploration, I'd suggest the following software packages:  xbat (Matlab program written by Harold Figueroa from Cornell - nicely done, no longer supported and documentation is supposed to be a bit on the slim side),. Cornell's Raven, Osprey (Matlab program written by Dave Mellinger at Oregon State that has some nice annotation features), or Triton (Matlab program written primarily by Sean Wiggins at Scripps Institution of Oceanography that is designed for very large datasets and has a nice compressed spectrogram function [some of the other packages have this as well]).  There are other packages out there as well.  For detecting calls, it really depends upon your soundscape and the calls you are interested in as to how easy that is to do.  The Teager energy detector was used by Kandia and Stylianou (2006) for detecting odontocete echolocation clicks and should work well for snapping shrimp; we developed it independently and while we never published as Kandia & Stylianou beat us to the punch our description can be seen in some our papers (look at Soldevilla et al. 2008 or Roch et al. 2011).  

I haven't done work on fish calls, but I would expect that for pulsed calls you would want to key in on some aspect of the frequency range and pulse rate. 

If you just want a general signal detector, you could look at the work of Erbe and King (2008), they designed a signal detector that is very easy to implement and can do a pretty nice job.

Best of luck in your research - Marie

Erbe, C., and King, A. R. (2008). "Automatic detection of marine mammals using information entropy," J. Acous. Soc. Am. 124(5), 2833-2840.

Kandia, V., and Stylianou, Y. (2006). "Detection of sperm whale clicks based on the Teager-Kaiser energy operator," Appl. Acous. 67(11-12), 1144-1163.

Roch, M. A., Klinck, H., Baumann-Pickering, S., Mellinger, D. K., Qui, S., Soldevilla, M. S., and Hildebrand, J. A. (2011). "Classification of echolocation clicks from odontocetes in the Southern California Bight," J. Acous. Soc. Am. 129(1), 467-475.

Soldevilla, M. S., Henderson, E. E., Campbell, G. S., Wiggins, S. M., Hildebrand, J. A., and Roch, M. A. (2008). "Classification of Risso's and Pacific white-sided dolphins using spectral properties of echolocation clicks," J. Acous. Soc. Am. 124(1), 609-624.

I'd add a couple open source software projects -- Audacity for basic stuff and PamGuard for more advanced analysis.

https://sourceforge.net/projects/audacity/

http://www.pamguard.org/

Dear Tulio,

I have been using R for some time now, and for me it has solved pretty much everything I need. I basically use two packages, seewave and soundecology.

https://cran.r-project.org/web/packages/seewave/seewave.pdf

https://cran.r-project.org/web/packages/soundecology/soundecology.pdf

Best luck!

Carlos.

Hi Renata, my approach to soundscape analysis is based on multiple levels:

1 - I produce long term spectrograms (1 whole day) with my own software (SeaPro) based on packing together spectra with mean values or with peak values for any frequency.

This way I have a global picture of the soundscapeand how it evolves through days, weeks and seasons. This way I can easily separate bad days (wind, rain, tech failures) and good days. In addition, I can see usual and not usual acoustic events.

2 - I also do hi-res spectrograms (ms to seconds) to reveal the details of acoustic events found on long tern spectrograms and recognize different shapes (e.g. bird songs).

3 - then, I calculate indices by using R with seewave and soundecology packages. However, I'm (slowly) moving to Python as new packages are developed.

4 - I explore multiple indices together with statistical software (e.g. with analyses such as PCA and LDA)

The attached paper has been produced with this procedure.

It would be great to share more info on this topic !

Gianni Pavan

You can try SonicVisualiser and Audacity.