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Acoustic is one of our biggest and most active groups, with members collecting, analysing, and interpreting acoustic data from across species, ecosystems, and applications, from animal vocalizations to sounds from our natural and built environment.

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Project Showcase: "Global Birdsong Radio" - A distributed edge-to-cloud acoustic sensor network using live streams

Hello everyone,I am a hobbyist software developer and nature enthusiast, and while I don’t have a background in ornithology or academic biology, I wanted to share an open-source...

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This is really impressive Avi! Would you mind sharing what kind of hardware the local ingestion + detection is running on? 

It's a fun experiment to think if this could be expanded to any live stream happening on any platform (Instagram, TikTok)

Avi

Hi Luke, 

The local hardware is just a laptop and a few SIM routers.

I guess the heart of it is the user experience, where the users can navigate their own choice of nature real time stream mosaic.

Avi

Hi everyone,

I have a major update: Global Birdsong Radio is officially Open Source! 🎉

A couple of months ago, I replied to @brian.maiyo mentioning that I wasn't quite
ready to share the code due to maintenance concerns and the sheer organic
messiness of the project. However, after thinking about the feedback from this
community, I decided doing a "scorched earth" cleanup, stripping out my personal
credentials, and packaging the system into a publishable state.

You can now view, fork, and dissect the entire v41.0 codebase here:
🔗https://github.com/Avi-wilddetect/global-birdsong-radio

Since my last post, the system has evolved significantly. Here is some
information on how it works under the hood and what is included in the
repository:

1. Hardware & Architecture Specs (To fully answer @luke.browne):

 - The Edge Node (Local): Runs on a high-performance Windows PC/Laptop. This is
   the workhorse. It runs the Python scripts, FFmpeg, yt-dlp, BirdNET, and
   Google Gemini. It manages the "Hydra Network," dynamically routing listener
   processes through multiple attached 4G/5G SIM dongles to evade YouTube API
   bans, using a PID algorithm to monitor "Network Heat" (data usage) so I
   don't blow through my monthly cellular caps.
 - The Cloud Node (Web): A lightweight DigitalOcean VPS running Docker. It
   hosts Nginx, a Flask API, and a PostgreSQL database. The Edge node simply
   beams up tiny payloads of metadata and audio/image proofs.

2. Multimodal AI & "The Predator Reflex" The system is no longer just audio. It
now runs Google Gemini 2.5 Flash alongside BirdNET.

 - It uses local pixel-variance motion detection to save API quotas, then
   passes frames to Gemini to identify wildlife with strict 2D/3D size and
   depth constraints.
 - The Predator Reflex: If the Audio Engine hears a high-confidence acoustic
   target (e.g., an Elephant rumble or a Lion's roar), it instantly bypasses
   the Vision Engine's dormancy state, forcing an immediate visual scan of the
   camera. If both confirm, it merges into a highly accurate "Multimodal"
   detection.

3. The GUI Ecosystem To manage the "firehose" of data across ~400 curated
streams, the Edge Node includes a massive desktop application suite built in
PyQt6. The repo includes the code for the real-time Dashboard, the Intelligence
Hub (for AI prompting and taxonomy management), the Discovery Radar (which
auto-heals dead YouTube links), and a Curation Studio (where I manually audit
mistakes and export them into an ML dataset for future neural network
retraining).

A Quick Note on Transparency & Disclaimers: To be fully transparent with this
community: while the architectural vision, logic, and system design are mine, I
am not a software engineer by trade. 100% of the underlying Python, SQL, and
HTML/JS code in this repository was written by advanced LLMs acting under my
direction.

Additionally, because this project uses a custom "4-Tier Stream Resolver" to
bypass YouTube bot protections (falling back all the way to Headless Chrome
network sniffing if yt-dlp fails), I have to include a disclaimer: This is
published strictly as an educational research tool. Using automated scrapers can
violate platform Terms of Service, so if you fork it and deploy it, you do so at
your own risk!

Thank you all again for the encouragement. I'd love to hear your thoughts on the
codebase, the adaptive SNR math, or the multimodal logic!

And of course, for those who just want to experience the end result without digging
into the code, the live interactive map is always running at 🔗wilddetection.net.
Feel free to drop by and see (and hear!) what the global network is picking up right now.
 

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discussion

alp-data: unified access to 35+ bioacoustic datasets 

Hi WILDLABS community! I am a Research Advocate at Earth Species Project and wanted to share an update on our latest work :)We just released alp-data, an open-source pip-...

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Hi Diane, thanks for sharing this valuable resource. It'd be great if you could add it to The Inventory, a wiki-style database for conservation technology tools, R&D projects, and organizations. The Inventory is a place for individuals to explore what technology is available for their work, how it is being used by others around the world, and what the conservation tech community would recommend. Thanks in advance!

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discussion

List of bioacoustics software

Edit: Since posting this over 4 years ago, we've moved it to its own GitHub repository and associated website. If you have any suggestions for software...

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Hi Tessa, thanks so much for the update!!! I love that there is an interactive website now. This is such a valuable website, I'm happy to see the updates ;) - Liz

Actually, on the subject of acoustics, the Raspberry Pi based sound localization system I developed has been running continuously since 2023 writing to a 256GB SD card :-)
 

https://github.com/hcfman/sbts-aru

I submitted it for addition to that list a few years ago. Should be there also I guess.

I have three of these running around my house. Off power though because I can. Actually I use one of them as a time server for all my computer equipment because it maintains microsecond time accuracy continuously.

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discussion

AudioMoths in Arctic conditions?

Hi all,I'm working on a project looking at seabird bioacoustics in Svalbard this August. We're hoping to capture diel activity patterns in Atlantic puffins, Little auks, and Black...

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Hi Barbara. 

If you are able to power the Audiomoth externally from a lithium-ion source, the lithium-ion batteries can be used down to -20 deg C. They can't be charged below 0 deg C though so if you were thinking of a solar charging setup as well, then you'd need to look at the temperature range you're planning to use them in. 

In regards to the battery life for continuous recording, I found this information from here:
 

The recording lifespan of the AudioMoth on one set of batteries has previously been measured only for a subset of the possible configuration settings. Hill et al. reported the battery life of the AudioMoth using 3000 mAh lithium batteries for some common configurations [15], reporting that an AudioMoth could record for 115 days recording at 8 kHz, the lowest sample rate, for 30 s every 5 min. The developers also reported the AudioMoth lasted 9 days recording nonstop at a 48 kHz sample rate. While the AudioMoth configuration app provides estimates of battery life for any chosen configuration settings, these estimates have not to our knowledge been validated empirically.

Even derating the battery life by a factor of 2 due to cold temperature conditions would seem to get you over the finish line. One experiment you can try is to record continuously in a refrigerator and look at the recording duration. Typical refrigerators are around 2 deg C which could approximate conditions you mentioned. 

If you will be using stock Audiomoths with no modification for rechargeable batteries, then I'd recommend using Energizer Lithium AA batteries which have some of the highest battery capacities for disposable batteries. Those are airplane safe if they go in your carry-on luggage. 

Hope that helps.

Akiba

 

Hi Maxi, thanks so much for this! That's really helpful to know. We were planning on using just a basic plastic windshield. Do you have any advice for what has worked for you against wind? Could I ask also, how long did your AudioMoths last on just regular alkaline batteries? 

Hi Akiba,

Thanks for this - that's very helpful! We're looking to borrow quite a few of our AudioMoths, so they wouldn't be modified or modifiable by us in any way. I will definitely try your idea to record in a fridge! Probably will have to be lithium AAs - thanks for the Energizer recommendation.

 

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discussion

Microphone for bird monitoring on Raspberry Pi

Hi, I am building a system to monitor birds (i.e. ~50-20k Hz) using a Raspberry Pi and I am looking for the best option for the microphone. I am considering: - a USB...

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apologies - ignore this reply (or if someone can delete it please do!) I have forum blindness and thought this thread was also about using Birdnet but I realise it isn't.... I suspect this is a setting on Birdnet-Pi so will post on their forums... 

Hi Paul, have been trying to get a i2c mic working with Birdnet but am having challenges in getting it working. I can install the adafruit mems you mention above and getting it working directly but when I try to use it through Birdnet I am failing. Have done some rebooting, testing, search forums but am drawing a blank - did yours work "out the box"? 

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

Hi — I came across this thread and wanted to chime in on the microphone selection question, as it touches on some tricky hardware trade-offs.

For bird monitoring in the 50-20kHz range, the MEMS route is actually more accessible than it might seem. The ICS-43432 from InvenSense is a solid choice — it's an I2S MEMS microphone, so it connects directly to the Raspberry Pi's I2S interface without needing a sound card at all, and it has good sensitivity and low self-noise in the relevant frequency range. No plugin power needed, no USB complexity. A simple breakout board (SparkFun or Adafruit have them) and a few lines of configuration and you're up.

If you want to avoid I2S configuration entirely and still keep costs reasonable, the ICS-40720 you mentioned is also excellent and can be used with a simple circuit — a basic op-amp buffer stage is straightforward to build and eliminates the need for a sound card entirely. I can sketch out a basic circuit if that would help.

The Audiomoth uses the SPM0408LE5H-TB partly because of its very low noise floor, but for a Pi-based system the ICS-43432 gets you most of the way there with much simpler integration.

What's your target deployment environment — indoor, outdoor fixed, or field portable? That affects the enclosure and weatherproofing approach more than the microphone choice itself.

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discussion

HELP! USB device code, not recognised by Android

Anyone on here good with USB device code - descriptors etc? I have this C++ / C (linked) which started life as an ARM mbed example for Raspberry Pi Pico. It's the firmware...

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I used your uf2 file to flash up a Pico (one I built, not the commercial one) and shoved a signal genny on GP28. It shows up in BatGizmo and correctly identifies and heterodynes the signal I put into it.

Does that help? If not, let me know, I do USB all the time. I'm developing a bat mic for a STM32 (just because I can).

Pengy

Hi — I came across this thread just joining the community. I'm an electronics and embedded systems engineer with 30 years of firmware and hardware design experience, mostly on STM32 and similar ARM Cortex-M targets.

The UAC2/Android issue is a known pain point. Android's UAC2 support has been inconsistent across vendors and API levels — it was only partially addressed in Android 5.0 and many OEMs never fully implemented it. Phil is right that if TinyUSB only exposes UAC2 descriptors, most Android devices will simply ignore the device.

A possible path forward without abandoning TinyUSB: some versions of TinyUSB do allow building a composite device or overriding the audio class descriptor manually. If you can post the relevant section of your usb_descriptors.c (specifically the audio function descriptor and the bcdADC field), I can take a look and see if there's a way to force UAC1 compatibility at the descriptor level without breaking the existing hosts.

Alternatively — and this might be the simpler path — have you considered a small STM32F0 or STM32G0 as the USB bridge? They have mature HAL USB audio support with UAC1 out of the box, cost under £1 in volume, and would leave your Pico firmware untouched.

Happy to help either way.

Love this advice down to the chip level 😀

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discussion

Safe and Sound project report: Is Camtrap DP a suitable standard for (bio)acoustic data?

Dear WILDLABS community,We are pleased to share with you the publication of the Safe and Sound project report: Is Camtrap DP a suitable...

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Your report on extending Camtrap DP to bioacoustics resonated with something we are just beginning to explore in Mindoro Island, Philippines.

We have ongoing camera trap deployments in interior forest habitats and are beginning to examine the acoustic layer embedded in those recordings, particularly for nocturnal species such as the Mindoro Boobook. The discussion around terminology and how datasets are structured feels especially relevant, though I am still trying to understand how frameworks like Camtrap DP would apply in practice to this kind of data.

It is encouraging to see this direction being shaped at the community level. I will be following this closely as we continue to learn and figure out how our own datasets might eventually align.

Thanks for this!  I've shared this post with the WildTrax (https://wildtrax.ca/) team and CanAvian (https://canavian.ca/) to investigate. We're exploring data standards as part of a recent initiative so this will be very helpful! @jeffcullis 

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discussion

🐸 WILDLABS Awards 2025: Open-Source Solutions for Amphibian Monitoring: Adapting Autonomous Recording Devices (ARDs) and AI-Based Detection in Patagonia

We’re excited to launch our WILDLABS-funded project to adapt open-source recording hardware and AI tools to help monitor amphibians, with an initial focus on one of South America'...

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🌿 Project Update — November 2025
Sharing our experience at the Symposium on Physics Applied to Ecology and Conservation (Foz do Iguaçu, Brazil).

We’d like to thank Carlos Araujo for kindly inviting us to take part in the Symposium on Physics Applied to Ecology and Conservation, held on November 6–7, 2025, at the PTI Campus – Universidade Federal da Integração Latino-Americana (UNILA) in Foz do Iguaçu, Brazil.

The event aimed to build bridges between researchers from different disciplines and countries, exploring how physics can support acoustic monitoring, ecological data collection, and biodiversity conservation.

🎙️ We joined Roundtable 3 — Hardware, Sensors, and Audio Recording, where we discussed:

Open-source autonomous recorders for biodiversity monitoring.

Energy-efficient design and sensor integration.

Alternative battery types and power solutions (particularly relevant to our developments)


🎥 Watch the roundtable recording here.


It was also a great opportunity to share our experience and highlight the WildLabs community, connecting with colleagues working at the intersection of physics, ecology, and technology.

 

 


 

 

Hi everyone!

Following up on our project development, we have just published the full report on our work integrating environmental monitoring into AudioMoth devices and the resulting BirdNET workflows for Patagonian amphibians. You can find the complete documentation and results here.

Beyond the technical implementation, we’ve documented the custom firmware, the AI training pipeline for our species, and the practical challenges we faced during field deployments:

Project Video: YouTube Video Link

Firmware: AudioMoth I2C Firmware Repository (GitLab)

AI Workflow: BirdNET-based Workflow for Amphibians (GitHub)

Edge Models: TinyFrog Repository (GitHub)

PyTorch reimplementation: BirdNET-Analyzer (GitHub)

If you are working on similar setups or have questions about the hardware or the workflow, feel free to reach out. I hope this documentation proves useful for your own research.

Thanks for the support and the exchange of ideas during these months!

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careers

Biodiversity Monitoring Scientist

This role would suit someone with a background in ecology or environmental science who enjoys combining fieldwork, data analysis, and applied research to support real-world environmental outcomes.

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discussion

Spectrolipi v2.0.1

Spectrolipi is a tool for visualizing sound, annotating spectrograms, and exporting ML‑ready acoustic datasets.Spectrolipi V2.0.0 is released now. Main new...

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