From Alex on Twitter:

Yes. The HydroMoth has the same microphone and firmware as the standard AudioMoth which is widely used for terrestrial conservation. You would need the standard green case which has a waterproof acoustic vent.

@Andrew_Hill Out of curiosity, since this campaign reached its goal & is ending soon, is it possible to turn the remaining Hydromoth units that didn't sell into Audiomoths for a limited GroupGets? I understand that Hydromoths can be used terrestrially as well, but they are more expensive than the last Audiomoth campaigns have been by $50+ so just wanted to see if Audiomoths would still be cheaper. Just trying to figure out if I should buy more Hydromoths or wait until Audiomoths start selling again (ends up being a trade-off between cost and time basically).

@Andrew_Hill I wonder if you could let us know more about the time keeping difference btw the regular AudioMoth and the HydroMoth (which is said to do better)?

Hi Carly,

has your issue been resolved yet? Could you link the stackoverflow question maybe? Otherwise this seems like a few lines of python code may solve this. I could assist with this if required.

R is usually not the obvious choice for file level operations :)

I don't know if this was resolved, but if you copy this code into a python file in the same folder as your .wav files and then run it, it should rename everything. Try testing it on a copy first in case something is off! This assumes that all devices are named "swift1" and all .wav files are in the same folder.

---------------------------------------------------------------------------------------------

#Reset timestamps for passive acoustic recorders

import os

from datetime import datetime,timedelta

from time import strftime

#Set Path to where you placed this file - Should be placed in folder with all recordings

os.chdir(os.path.dirname(os.path.abspath(__file__)))

#Start Date YYYY-MONTH-DAY and Time HR:MIN:SEC

start_date = "2022-06-17"

start_time= "T09:30:00"

curr_time = start_date+start_time

time = datetime.fromisoformat(curr_time)

incby = timedelta(minutes=20)

#For all .wav files in folder, set name to updated YYYYMMDD_HHMMSS and the increment by 20 minutes.

for file in os.listdir():

    tnameform = "swift1_"+time.strftime("%Y%m%d_%H%M%S.wav")

    if file[-4:] == ".wav":

        os.rename(file,tnameform)

    time = time + incby

If only I didn't work in rainforests where canopies preclude solar panels.... *sigh* lol

Went down a rabbit hole reading the battery threads on here. It strikes me that gravity batteries might be a good solution in rainforests. String a line over the highest branch you can and then raise as much weight as you can. I'm unfamiliar with the power requirements of camera traps / audiomoths but I might do some napkin calcs if i'm bored this week. @carlybatist 

Hmm 20kg raised 5m would only buy you less than half an AA battery assuming 100% efficiency. Lame, so much for that idea.

Hi David,

I can build him one per requirements.  I'm getting started on the solar powered variant.  If your scholar is in the Beijing area, I can put him in touch with a colleague who does this sort of work.

Jamie's tip piqued my curiosity, so I bought what I thought might be what Jamie was talking about.  It is an SK-001 as detailed here (not my video) for SGD4 or thereabouts.  The price alone boggles the mind, though there are some characteristics that should be borne in mind if used for conservation work.

Foremost is the use of MP3 rather than raw uncompressed WAV which make comparing this data with existing recordings challenging.  Second, an electret condenser mic is used, so manual calibration of recordings may be required  (MEMS mics OTOH have stricter characteristics that are documented).  Third, it is unknown what kind of AGC or signal companding is used (this is in addition to data compression).   Since the device is advertised as a voice recorder, the presence of AGC/companding/noise gate features would not be surprising.  Also, I could not get it to work with a 64G micro SD card, but 32G was ok.

The SK-001 device could be useful for presence/absence type of PAM.  For other work, more testing would be needed to see if this device would be suitable.

Once again, I'm referring only to the SK-001 that I bought that I thought Jamie might be referring to.  These comments may not apply to other low cost recorders.

There are a bunch of different options for detecting calls in audio data, from proper statistical platforms such as R/Python, to bespoke software such as Arbimon, Kaleidoscope & Raven. Edge Impulse also an online ML model-building interface, but this is more focused on then deploying the models onto devices for edge computing. Arbimon has template matching features that are a good way to start finding detections to build a training dataset, I have used it for this in the past. Arbimon is online & free. Kaleidoscope has a clustering function which is again a good first step to start picking out the low-hanging fruit of detections so to speak. It's a desktop app, but this is not free ($400/yr). Raven also has some automated features -  template & band-limited entropy detectors. It's also a desktop app and not free ($100-$800 depending on 1-year or permanent license and whether non-profit or not; not sure where a government agency would fit into that). 

There is always the ubiquitous split between biologists who traditionally are taught to use R and tech/computer folks who are taught to use Python, but for ML, Python's ecosystem is really well set up. Not sure what the level of programming you/your dept has, but there are a TON of free resources online for learning it if you were interested.  

Relevant Python bioacoustics packages potentially of use - Acoustic_Indices, scikit-maad, Ketos, OpenSoundscape (as well as the obvious ML ones such as TensorFlow)

Some R packages as well -  soundecology, bioacoustics, monitoR, warbleR, gibbonR

@tessa_rhinehart has created a fabulous list of bioacoustics software that you can find here: https://github.com/rhine3/bioacoustics-software. 

You can also turn to articles that have already done similar things and reach out to the authors to discuss their methods. I've got a (totally un-exhaustive) list of papers on passive acoustic monitoring, with a section on 'analyses' that you might find useful to start with; I can email it to you if you'd like. Working on a PAM training materials page on my website that it will be available at shortly as well (will post the link to Wildlabs when it's live!).

Hope this is helpful!  

Hi, 

Look at this publication (below) and download the BirdNet app. The computer code is provided to train ML algorithm that will allow  you to tailor the model with your own data. 

Thanks, Mrigesh 

I can understand the reluctance to go down the I2c route on linux, but I bought some Adafruit MEMs mics (https://www.adafruit.com/product/3421) and they work fine.

If you want to dip a toe in the water, are not too cost concious, but want something you just plug in and it (largely) pretty much works, then I'd recommend looking at the seeed respeaker. Its an array, and is probably overkill for what you want. It does come with quite a bit of hand holding though, I got a "4 mic linear". Connect some jumper wires, screw on a board, install some software (!). OK for a POC (proof of concept) and as a sanity check.

Going back to the Adafruits mems breakouts, I have them wired back to the pi by re-using old CAT5 network cable, so the pi can be in one place and the mics somewhat remote - eg round the other side of a tree?  I configured a lot of software to get this working as I wanted, but it essentially removes "white noise", does file compression etc.   

Hi Julien,

I use this mic for my raspberry pi setup and it works well picking up bird species singing in our garden while the mic and raspberry sit under our roof tiles.

Greetings,

Robin