Hello Folks, I work with the Dept of Conservation, New Zealand (NZ) government. We are eagerly looking for a Machine learning or AI solution to identify the NZ birds (and other species in future). Department has historical audio/acoustic recordings gathered by our rangers; we want a solution to scan these acoustic files to identify the birds. This solution is essential for us, and the department is willing to invest a reasonable amount in enhancing/developing the existing/premature solution, which can also be made available to other conservation groups in NZ. Government has a target of predator-free NZ by 2050, and this solution and similar others in future will help us reach that target.
I am new to Wildlabs.net and have seen some cool and advanced technologies used in conservation space I am extremely excited to implement them into our organization. I shared some of these capabilities in our organization, and there was an interest and curiosity. In that direction, I have been asked if we could find a solution to our audio/acoustic files.
Please let me know if you have a solution to this problem and are willing to collaborate to take the solution to the next level. My email id is [email protected]; feel free to email or message me for discussion.
Thank you so much.
Jitendra
10 June 2022 6:44am
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
Carly Batist
WildMon
11 June 2022 1:16pm
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!
16 June 2022 9:27am
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
![](/sites/default/files/embeds/bdf15010d7b0fbb61d24dc2a419f73ec.gif)
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 …
20 June 2022 1:00am
Thank you @carlybatist , @Freaklabs and @MK . The inputs are very useful and I am progressing on my project based on that. Appreciate a lot.
Akiba
Freaklabs