HI Alasdair,

pardon me if this is irrelevant, do you know any device which we can use to record low frequencies like sounds of elephants etc? I don't think AudioMoth is capable. I'm just trying to find such a device/technology. 

kind regards!

Nilaksha

Paul,

Hi - is this still needed, as I'm sure we could provide something relating to birds, bats and cetaceans?

Thanks,  Carlos

Hi Arik

Did you already test it with wolves?? I would be very interested in learning your results as I am preparing some accustic monitoring of wolves for my PhD.

keeping in touch

Nuno

Hi guys, interesting discussion. As you may have read, we are developing an elephant-detector (to avoid HWC's). The technology we are using (neural networks)  can also be used to learn to recognize wolves automatically. If you have wolf-sound samples for us, we can create a classifier to automatically process your recordings. Alternatively we may be able to run the classifier in real-time on a RPi. But like you mentioned above, power consumption is an issue. Maybe less so if we can mount the sensor on a street light with car battery or something. What do you think?

Hi Courtney,

sounds are indeed a very interesting theme. If you ask me, it is one of the most underestimated senses, especially when we are talking about wildlife protection. People tend to make quite a lot of noices that distinguish them from animals ;-)

I am working on a prototype 'soundscape sensor'. The basic idea is to record all sounds at a particular location and calculate a kind of 'normalised' sound. This summary can then be used to listen to sounds that deviate from that. Could be chainsaws, gun shots, car engines, talking people, barking dogs, whatever. 

By feeding these sounds to rangers with local knowledge, or to a crowd (like Panthera is doing), the sensor's performance improves over time. 

Although we think our first prototype will be ready by the end of this year (recognizing one particular sound within an outdoor environment), subsequent steps are quite challenging. Especially when more than one or two sounds occur at the same time.

If you ask me where we will be in the next few years, I would say:

2017: recognizing any trained sound within a given field context (using a Raspberry)

2018: learning to distinguish compounded sounds (using a backend server)

2019:  idem, but then much more efficient, so we can run it on a Raspberry pi

Interested to help us to create this? 

Cheers,

Jan Kees

My moonshot would be increased used of DSP in underwater acoustic monitoring, to enable small arrays to filter out engine noise to look for vocalisations; statis reflections of exiting noise sources, and doppler reflections too. Heavyweight DSP might be enough to gauge the size of objects and a useful distance estimate too…

Hi Courtney,

Jason mentioned some very good journals (and there are many more) that cover broad areas of research as indicated by the journal titles. Perhaps your paper might fit into any of them.

However, people can give you more focussed recommendations if you tell us what research questions you addressed. At this point we only know what kind of technology you used.

Is technology itself the main topic of your paper? Or are you reporting what you learned about tiger biology/ecology through using accoustic technology?

Julia

Hello Courtney

My turn to apologies for delay. Your study sounds very interesting and potentially a valuable technique for future in-situ research. Seems it could indeed fit journals with a broad scope concerning wildlife ecology and conservation (including those mentioned earlier in this thread) and not narrowly limited to accoustics. I hope you are progressing towards publishing your work and I look forwarrd to reading it!

Hello all- When there are future updates with this project, please do continue to post them to this thread as it progresses. Thanks! The community would love to see updates.

For those who are interested in this thread, Arik has posted his new prototype in a new thread, where the discussion has continued. 

Hi, 

I think Steffen provided a good summary of the challenges. 

IMO, the two most promising methods of estimating animal density from acoustic monitoring are the "generalised random encounter model" (gREM) and an extension of spatially-explicit capture-recapture (SECR). These potentially solve Steffen's problems 2 and 3, i.e. linking acoustic counts to abundance (2), and converting abundance to density (3). 

gREM, although theoretically possible, may be tricky in practice (especially obtaining an estimate of how wide an animal's acoustic signal is). See here:

http://onlinelibrary.wiley.com/doi/10.1111/2041-210X.12346/abstract

SECR is potentially very powerful, but depends on an independent estimate of calling rate (a problem Yu Shiu rightly picked up on). I think this would be entirely possible for a species you can find and observe (e.g. a frog or common bird species), but difficult for lots of cryptic, low density species (e.g. tigers!, as Courtney mentioned). See here:

https://www.researchgate.net/publication/272371302_A_general_framework_for_animal_density_estimation_from_acoustic_detections_across_a_fixed_microphone_array

However, gREM and SECR do not help with Steffen's first point (1) "quantifying the number of vocalisations from a stream of acoustic recordings". Others might be able to advise on the best approaches there. Perhaps this is primarily a software / data processing problem...?

In terms of sampling design (as Mariane and Courtney were interested in), it depends what your aims are. For occupancy (which is not equal to abundance/density), similar design principles to camera-trapping are fair (but taking into consideration Yu Shiu's point that the effective sampling area might be MUCH larger for an acoustic sensor than a camera trap, so camera spacing will have to be larger too). For gREM, you can fairly flexible about sampling design (repeated detections of the same individual are not a problem), but your sensors should be set randomly in space (with respect to animal movement), not along trails etc. For SECR, you don't have to set your sensors randomly, but sensors must be close enough together for repeated detections of the same call in multiple sensors simultaneously (this design constrasts, therefore, with an occupancy design). 

Hth,

Ollie

Hi Stephanie,

This is an interesting thread. For those interested in the topic, and forgive me for the blatant self-promotion of work, a Biological Reviews 2013 paper on the topic can be downloaded here   http://www.creem.st-and.ac.uk/decaf/outputs. Additional case studies papers as well as a more general public paper in Acoustics Today are also available for download from the link. This link is the outputs page from project DECAF, dedicated to estimating animal density from passive acoustic fixed sensors, using cetaceans as case studies. The methods have been far more developed and used in the cetacean community, but I suspect a wider use in terrestrial environments will occur in the coming years. A key hurdle is perhaps the dynamics involved in acoustic cue rate production, but the issues will be much easier to tackle in terrestrial environments than in the marine environments we have been working on.

Hope this is helpful,

Tiago