Hi Jitendra. 

I recommend checking out these videos from the WildLabs TechTutor series and perhaps contacting the speakers for advice and recommendations:

Jamie Macaulay: How do I analyse large acoustic datasets using PAMGuard?

Zephyr Gold & Marconi Campos: How do I use pattern matching to label acoustic data with RFCxArbimon?

Good luck with the project. Hope to hear more about it as it progresses :)

Akiba

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 

BirdNET: A deep learning solution for avian diversity monitoring

Variation in avian diversity in space and time is commonly used as a metric to assess environmental changes. Conventionally, such data were collected …