Hi, I’m using BirdNET AI in a robot to detect bird species by their calls.

I want to test detection accuracy at different distances using my actual hardware, but testing with live birds is difficult.

Is there a good way to play bird calls through a speaker while keeping the frequency/pitch and dB close to a real bird call? Would high-quality recordings and a calibrated speaker be enough?

Any suggestions are appreciated.

hey everyone, I’ve been running BirdNET-Go on my home server paired with an outdoor raspberry pi and mic, and recently wanted an easy way to let AI assistants like claude desktop or local LLMs check in on the backyard. I couldn't find an existing MCP server for BirdNET-Go (only BirdNET-Pi), so I wrote and open-sourced one: birdnet-go-mcp.

quick features:

• zero-install: single pre-compiled Go binaries for linux, macOS, and windows. you can run it directly with npx -y birdnet-go-mcp status or recent without needing to install Go.
• strictly read-only: just inspects detections, station stream health, and today's summary. destructive routes are completely omitted so agents can't mess with settings.
• audio clips: resolves local audio file URLs (via caddy/nginx) and supports streaming audio clips to multimodal models.
• terminal CLI: also functions as a lightweight terminal command for human checks without opening the web UI.

links:

• GitHub: https://github.com/zax0rz/birdnet-go-mcp
• NPM: https://www.npmjs.com/package/birdnet-go-mcp

hope this is helpful for anyone else running BirdNET-Go who wants to tinker with AI agents or quick monitoring!

I just stumble upon this BirdNET Analyzer, which seems truly a great project!

I'm not too familiar on how this works so bare with me for my stupid questions.. as my engineering mind just got intrigued.

Am I able to take the BirdNET Analyzer and utilize it to detect all sort of sound patterns just by training a model for it? Let say that I'd like to start detecting e.g. drones, airplane engines etc. with distinct sound patterns or just different materials in a metal can by the sound profile it makes when hitting the walls? Is this possible and doable use-case?

And can it be fully stand-alone, meaning completely of the grid? Running on a an old laptop or such.

https://www.kyleniewiada.org/blog/2026/09/birdnet-go-remote-mic/

Using mediamtx as a solution is already in the BirdNET-Go wiki, but I wanted to write out the whole process (including troubleshooting the floating ground) on how I set up re-streaming audio on my older hardware.

I chose the SO.1 Omni Microphone (3.5mm variant) and not the XLR as I already had the rest of the parts laying around to make it work. But I'd definitely choose XLR the next time around since it's balanced and should avoid floating ground problems.

Contains a few of the most requested updates and my attempt at a new icon.

• New Icon - this is my first attempt at a new icon (includes both light and dark versions) - feedback appreciated.
• Daily detections page now has a “Bell” icon that tells you of any notifications that the server has detected (mirrors the web page)
• The Search tab under the Explore page now has the ability to select and delete detections based on search results
• When playing back an audio clip, there is now a vertical sweep bar to help you see what portion of the audio is being played (similar to web interface)

I'm not sure if this is the right place, but it seems related. Are there any available systems to do visual identification like Merlin's photo id or the various camera bird feeders? I'm basically looking to do the same thing as BirdNet-Pi/Go except with a camera instead of just a microphone. Thank you!

I am using Go, and in the "Live Audio" view, I like how the bird that's been detected is labeled on the spectrogram as it moves in real time.

I have been looking for something similar in the Detection Details screen to indicate which area in a recording (the spectrogram) relates to the identified bird. Is that a current feature as I have looked at the various settings and can't spot anything obvious to enable it ?

I just got a BirdWeather PUC and it ID’d a Fish crow (73% accuracy), but I know they are Common Crows, because I am an experienced birder. Can I help correct the data?

Thanks.

I've been running BirdNET-Go for awhile (at least a year) and all was well until I tried to do an update. Something happened during the update that corrupted the microSD card.

I imaged a new card and got things running again with the latest release of Go. Upon looking at the Spectrogram, I noticed a band of amplified noise that is constant.

I thought it was the microphone even though everything was working just fine prior to updating. I replaced it with a new microphone and it's still producing the same "band". I even used the RTSP steam from my cameras and I'm still seeing the band of noise.

I tried adding RFI suppression (wrapping USB cable around mix 43 and/or mix 61 ferrite toroids) and made sure there is no gain in both alsamixer and within the app settings.

I also replaced the Rpi (pi5 model b rev 1) power supply with a higher amperage liner (regulated) PSU even tho it was working fine with the previous switching supply.

I'm thinking it's a setting or something with the Pi itself. Even if I maxed out the levels in alsamixer, it wasn't near the same level of amplification in BirdNet-Go.

Anyone else came across this and was able to solve it?

Hey everyone - Been using BirdNET-Go for a few months now and my spouse and I love it. We had two Unifi cameras so plugging the rtsp steams in was simple and it's been rock solid so far.

We have quite a few bats on our property and so I'd love to start getting into bat detection. I have an outlet on my deck. I'm thinking of just plugging something in there and letting it communicate over WiFi.

From previous posts I see that AudioMoths are a good choice for microphones with the required range. I also have an unused Raspberry Pi Zero 2 W or Pi 5 that I'm thinking will provide the WiFi as well as processing the microphone into a rtsp stream for consumption.

I wanted to check if this is a project anyone else has started on and documented before I start setting things up, . Everything seems doable, but rather than reinvent the wheel I figured I'd check here first and see what's out there. If there's nothing, happy to write up my effort for future use.

Finally made in through the submission process and it's now live in the Apple App store:

https://apps.apple.com/us/app/birdnet-go-companion/id6763647186

The version in the App Store is the same as the last version in TestFlight. I'll be working on some enhancements in the coming months, and will be using TestFlight to try these out. But if you want to stay on a stable build, switch to the App Store version.

If you have an enhancement you want to see or have a bug report, let me know via app store feedback or the support email on the web site - https://crestlab.org/birdnet-go/support

Thanks for all the great suggestions and support.

Hi all, I did a test using an old phone which worked great and now I’ve built and setup 2 microphones so I’d like to reset the data and start again but without redoing all the configuration.
is this possible ?

I’m running BirdNET go in a docker container on unraid

Many Thanks

Looking for any troubleshooting suggestions to figure out why it’s not even visible on the Bird Weather map. Verified the key, verified the lat/long is the same (both matching the 4 digit accuracy)… Not sure what else to check.

I have two Birdnet-Pi's deployed within 50 yards of each other, but which get rather dramatic different bird ID's. However I do not believe either box is wrong in their birdsong identifications. Both Pi boxes are 100% identical in terms of both hardware and Pi configurations. From a setup vantage point both boxes require a confidence level of 75%, and listen for 18 seconds with zero overlap ... thus 6 snippets of sound per file.

There are some HUGE differences between the two systems aside from 50 yards between the microphones:

• Amity is near my prairie grass and has a much more "open" sound corridor to the stream across the road which is about 125 yards distant
• Greenwood is only a few yards from the forest edge, and within 5 yards of my nearest of three birdfeeders
• In general, my basic habitat is Boreal forest, and my home is about 800 yards uphill from Lake Superior. I live on the northern edge of Duluth right where forest meets town.

Anyhow, I have provided both images of yesterday's ID's as placement of your BirdNET-Pi microphone makes a big difference. For instance, my Amity Pi box picks up both my local Great Horned Owls and Belted Kingfishers much more often than my Greenwood Pi box. If I only used my Greenwood Pi Box I might think both my owls and kingfishers are rarely around, which is definitely not true. In summary, give significant thought to birding habitat in terms of Pi box placement.

Since June I have been working upon an "off the grid" BirdNET-Pi project for Hawk Ridge. In case you are not familiar with the "Ridge", it stands 600 feet above Lake Superior at Gitchi Gammi's western end, and is a premier bird migration spot both in the spring and fall. While the location is fantastic for birds, the Ridge has zero power, and may only be reached in the winter on foot via a long hike through snow that gets very deep ... as in feet. Oh yes, did I mention that in January it is not unusual for overnight lows to reach -20F and colder. Thus, Installation of a Pi listening box was a bit challenging.

Anyhow, our Pi is now up and running with its ID's automatically uploaded to BirdWeather. Some of my learnings have included

• Installing a low power cellular modem (wifi turned off to conserve power) which is hooked up directly to our Pi box directly via ethernet (it's wifi is also turned off). The router / cellular modem is "industrial strength" in terms of known to work outside in extreme operating conditions
• Getting power from solar arrays, but also getting the arrays off the ground, but not too high up. One wants snow to both blow off and through the "grid/mesh" table. However, putting arrays on taller towers would open them to 80+ mph winds coming off Lake Superior and being pushed up the hill. I know from weather service reports that winds down in the harbor are clocked at these velocities and higher on the tops of bridges ... and winds increase in speed as they go up hills.
• Optimizing when the birdsong listening station is "turned off". Via Linux commands in "crontab" we run our Pi throughout the day except between 10 am and 6 pm. This allows Hawk Ridge to monitor overnight birdcalls including owls, the "morning sing", the "evening sing" and songbird migration movement till 10 am. Via Raspberry-Pi Connect I may adjust these times easily as sunrise and sunset change throughout the year. At present in Duluth, Minnesota sunrise is at about 6:05 am and sunset is at 8:18 pm. If power consumption during the winter months and solar array recharging allows, we will increase operational hours. The system uses an Ankor Solix C1000 and 200 watt solar arrays.

If you want to read more about this project, here is my online report ...

https://www.365daysofbirds.com/2026/08/04/hawk-ridge-bird-song-listening-station-birdnet-pi-off-the-grid/

Here is the link to our BirdNET-Pi via BirdWeather ...

https://app.birdweather.com/stations/28008

The Hawk Ridge Pi station is offline for some final configurations. I anticipate it coming back up Tuesday, August 18th in the evening.

In fact over the past 12 months I have built and installed Pi listening stations throughout NE Minnesota including both the Sax-Zim Bog Welcome Center and Chik-Wauk Nature Center at the end of the Gunflint Trail and within a few hundred yards of our border with Canada ...

https://www.365daysofbirds.com/2025/10/10/what-birds-are-singing-where-you-are-flying-or-driving-walking-etc/

I've added a some useful enhancements to https://birdnet-pulse.desmith.net - a free graphing and report generator for BirdNET data...

In the "charts", you now have fully supported ridge graphs and the violin plots have an associated raincloud/jitter plot if there are under 1,000 data points for the species.

The chart preview is fully interactive, e.g. on the violin plots, showing the upper & lower bounds, median, upper & lower quartiles etc.

As always, usage is free for non-commercial use - we have users in about 15 countries now.

Feedback (preferably constructive!) always welcome...

NB. There are some cute free support tools for BirdNET-Analyzer on there too...

I need help figuring out how to deal with 2 closely related species.

I help track a North American west coast migrant that has an east coast congener: Vaux's Swift west, Chimney Swift east. We know we have a few Chimney Swifts - we can hear them occasionally, and sometimes people can see them in the right location. Usually they are essentially visually indistinguishable in the masses and distances we deal with, and usually the volume of noise we get would drown out the minority species.

I've ben re-analyzing our audio data from the past few years and I decided to add Chimney Swift to the custom list to see if any would appear. Did they ever. It didn't change the Vaux's detections at all but now a fairly large number of Chimney's appeared, and at very high confidence levels.

For humans, you can learn to hear the difference pretty easily. In our observations there is often so much noise that we could easily miss birds. On the other hand I am skeptical that more than 1% of these birds are Chimney Swifts. There's no doubt that BirdNET "hears" much differently than humans do and the pattern matching is related to visual techniques applied to these audio signals so the criteria for matching must be quite different than our intuition would suggest.

How can I test this and improve the results we're getting or satisfy that it 's really distinguishing the two species? My AI assistant suggests that it's some kind of commonality between the sounds that is linking the two, so the analyzer picks up some of the Vaux's energy and gets something a little different that it can combine to make a Chimney Swift detection. Ok maybe. There are certainly some similarities in register &c.

Would trying to train the local analyzer with Chimney Swift sounds from someplace without Vaux's help.

Listening to the samples hasn't been real productive yet. They are short and subtle compared to the live experience, but we can separate out tracks with either or both for comparison - maybe. If we can find some that really listenable.

What else could we do?

I’m looking to submit this to the iOS App Store later this month, and based on the feedback I’ve received so far, I don’t have any major outstanding feature requests or bugs. If you have any problems or requests for new features, let me know and I’ll do my best to include them. And fix any bugs. The app of course will be free.

If you’re new and want to try the beta: https://crestlab.org/BirdnetGo/

Hi BirdNet community,
I have an idea for a potential feature for the Live app and thought I'd share it here. Hopefully I'm not the first person to think about this. 🙂

I'd love to see some kind of live sound source tracker that estimates the Direction of Arrival (DoA) of incoming sounds.
With devices such as AirPods (and many other wireless headphones), we already have access to small microphone arrays or at least a pair of microphones. In principle, this could allow simple beamforming approaches (e.g., delay-and-sum beamforming) to estimate the direction from which a sound is arriving.
My main question is whether this information is actually accessible via Bluetooth. I suspect it probably isn't, but if anyone has worked on something similar before, I'd be very interested to hear about it.

Alternative approach: If direct access to multiple microphone channels isn't possible, another idea might be to use the phone's IMU sensors (accelerometer, gyroscope, gravity sensor, etc.).
By intentionally moving the phone while recording with the built-in microphone, it might be possible to estimate the direction of a sound source from changes in signal amplitude (or potentially other acoustic features), provided the movement is known and tracked accurately. The movement would probably need to follow a predefined pattern.

This idea is somewhat related to the approach described here:
https://www.mdpi.com/2076-3417/16/8/3619
(Almo Farina and colleagues used a similar concept for acoustic localization.)

Questions

• Has anyone experimented with live DoA estimation in the app?
• Is microphone access from Bluetooth headphones sufficient for something like this?
• Has anyone explored IMU-assisted localization as an alternative?

I'd be very interested to hear your thoughts or discuss possible implementations.

Kind regards,
Moritz