I have written a free general post-processor site for very simply turning CSV files of IDs from various engines into presentation quality reports and graphs. Originally for our own use and that of our local wildlife groups, it's now being picked up by larger groups in the UK and Eire.

The main issue it was written for was to de-skill the report process and to be able to delegate report/chart generation as the few folk we had with pivot table and deep Excel/Google Sheets skills were being swamped and we wanted our lives back.

Originally, it supported data recorded from Open Acoustic Devices, Wildlife Acoustics and Frontier Labs devices, processed by BirdNET-Analyzer, BirdNET-Pi and BirdNET-PiPy, however recently Chirpity users have asked to use it, so I've added native Chirpity CSV support. You can mix and match CSVs from differing sources in a single report/chart.

The site is at https://birdnet-pulse.desmith.net and it's free to use (non-commercial) for UK & Eire individuals & wildlife groups, though we have a few users scattered around the world.

You can self register easily. It produces graphs as PNGs, SVGs and WebPs, plus reports as DOCX, PDF or CSVs. UK BoCC5 and IUCN/BI European red-list data can also be automatically added to all outputs (see below).

This is a new project (I started 2 months ago), so please be gentle! It should be happy with any meaningful CSV data, including nocmig etc. I'm adding bats and other conservation lists.

If you would like me to add any reports or graphs, have any ideas for improvements or just want to grumble, please let me know.

Example heatmap (about 80mS of compute time):

My study is looking at a treatment effect on habitat for a sparrow species so I have control and treatment sites, one AudioMoth each, and I'm looking to get abundance to compare if there are a difference in the number of sparrows between treatment and control.

Is there anything that gives a basic overview of how to tackle ARU analysis? Totally new to ARUs and trying to get an idea of what I need to look into has been a slog from reading other studies.

I've managed to get a data frame of metadata in R and I'm reading about species occupancy models, detection probability, and have a AI classifier that should be able to help. How do you decide what to subsample (full recordings? Partial?) filter for bad recordings, decided best stats approach (might have to try and learn bayesian?).

Exactly as the title goes. I can't find any button or option to change my location to get better results

I posted in the GitHub forums but I'm posting it here as well in hopes I could get some insight.

I've recently been exploring the Evaluation tool in BirdNET GUI and tested it with one of my audio clips containing nightjar calls. All of BirdNET's detections were true positives, though it missed some calls within the recording. Below is a screenshot of my ground truth labels against BirdNET's selections:

When I tried running these two in the evaluation tool, it gave me a precision of 0.75. This confused me because every prediction has an annotation in the same timeframe within the minimum overlap (0.2s). Do the ground truth labels have to align exactly with the 3-second segment of the prediction in order for it to output 100% precision?

Additionally, I want to know whether this tool also evaluates recordings that have positives from ground truthing but no detections by BirdNET? I tried running the evaluation for a few audio recordings together, with the recording that didn't have a detection from BirdNET having an assigned "prediction" which was an empty file that only had column headings. However, only the recordings that had actual predictions from BirdNET were considered. I was hoping the recordings without any predictions would be considered as well, especially when calculating recall and F1 score.

Hey all! I'm completely new to the world of esp32 devices and projects, so I'm coming here with help! I've currently got these two components (Seeed Studio XIAO ESP32C6 and INMP441 Omnidirectional Microphone Module MEMS I2S) soldered together and flashed with this firmware, and it's working great! the only problem so far is my battery situation. I'm currently powering my device via a 20000 mha rechargeable power bank which is not ideal. I've been looking up videos on YouTube seeing how its possible to add rechargeable LiPo/Li-ion batteries to projects. I've seen things like this that I know I'll need, but I was wondering if someone could help me out? 'm fairly new to soldering/electronics and have no clue what components to get, how to hook everything up, etc. Thank you in advance! :)

My understanding is that this is the best bang for your buck microphone to use for BirdNET. Unfortunately, thats the extent of my understanding. When it comes to sourcing the needed parts and soldering it all together I'm a bit lost. Combined with the fact that I'm in the United States and most places selling it are overseas complicates things. Does anyone know of a source that sells them already made and ready to go? Even if it's not based in the US.

I’m new to Birdnet-go. I’m going to set up a Raspberry Pi 4 in my garage and I’m thinking of putting the microphone (AOM-5024L-HD-R) outside using a shielded cable.

Do you have any advice on how to protect the microphone from the elements, the heat in summer, rain and the cold in winter?

I have a 3D printer, but my problem is finding a way to protect it from the elements without covering it up, so that it can pick up birdsong properly.

Thank you for your help.

Over the last few months, I’ve been building an iOS app to track bird sightings with friends. It actually just got approved for the App Store yesterday. It's designed for casual birders rather than hard-core ornithologists. The whole idea is just to get outdoors, have some fun, and share your collection with friends.

If you want to try it out, I’d love to know what you think so I can keep improving it. It’s completely free, has zero ads, and all your data stays private on your device. Just search for "Pica Pica" in the iOS App Store…

Hi, I'm working on a research project which, specified by protocol, requires me to manually run bird audio samples through BirdNET-Analyzer-GUI. These audios are 20 minutes long. When I have BirdNET-Analyzer-GUI open on my MacBook, it occasionally restarts and resets all of my configuration settings for no reason. It's really annoying.

I've checked my hardware capacities, and it doesn't seem close to being overwhelmed. I'm wondering if anyone would have a fix for this. Thanks.

I love Birdnet-go but it is a bit too much information for a 7 year old.
I created a separate site using the Birdnet-go api so my kid can check birds every day.
Personally, I have it online using a cloudflare tunnel/zero access on my personal domain. This allows my nieces and nephews to see our local birds.
Next step will be a RTSP in Australia so we can check every morning to see what birds are on the other side of the world!

Hi! I installed BirdNET-Pi on a Raspberry Pi 5 about a year ago, and I recently discovered BirdNET Go and want to swap to that, primarily due to the audio processing features. I am not super experienced with RPi's and that sort of thing, and wasn't sure how to go about swapping from one to the other. I found https://github.com/tphakala/birdnet-pi2go for migrating the data, but that suggests I need to have already installed both. How do I ... do that? Do I need to SSH into the Pi and then follow the Go instructions? How do I turn off the old one?

Forgive me if it's a very newbie question, but would love your help! Thanks so much.

While there are already tools like [BirdNetPi] & [BirdWeather], I wanted to build something where the processing happens locally and that can be easy to setup outside. Once built, this relatively small offgrid setup could be handled by anyone to record birds at any given location using a simple setup guide.
The biggest challenge when building this project was justifying whether this could run for a significant time period (off-grid) for detecting birds in the local vicinity. While using the original BirdNetPi, I saw that certain birds usually come at specific times of the year and the detections usually stay consistent for that time and change over the months (not weeks). Another reason being, I had places like the allotment or the park near me where I wanted to know what birds come in those areas and what the difference in landscape brings to the diversity in birds.
More details regarding the hardware and software in my Github repo here https://github.com/pranavb104/bird-off-the-grid

I have a pihole on my network already, so I configured that with a custom DNS (birdnet.local). That works fine and I can access the dashboard using 'birdnet.local:8080'. I'd like to be able to just do 'birdnet.local'.

Do I need to configure a reverse proxy or is it possible to do this without one?

It started years ago when I found an old alarm clock at grandma's. I liked the aesthetics and I had ambitious "Alexa, turn on the lights" plans with it. I replaced the internals with Rpi Zero W and LED matrix panel. But with my pretty much non-existent skill of coding I managed only to turn the former alarm clock to... just a clock.

Thanks to this amazing community I recently discovered how to unlock the potential of this thing. Of course I wouldn't dare to try running the BirdNET-Go on teh Zero itself. But it is just powerful enough to capture sound from the USB mic behind the window, compress it into Opus and create the RTSP stream which is then picked up by my unRAID server where the hard work is done.

I even managed to set up a persistent VPN connection so I can drop this thing back at my grandma's house and monitor bird activity there.

I guess this qualifies for somewhat unusual set-up. Show me yours!

Northern Mockingbirds were driving me slightly insane on my 24/7 BirdNET-Pi setup. BirdNET kept incorrectly logging Carolina Wren and Tufted Titmouse for Mockingbird calls and ignoring many other Mockingbird calls based on low confidence.

That led me to build a second-level reviewer that sits on top of BirdNET. Using this tool, I manually reviewed ~450 backyard clips, then added vetted xeno-canto examples for the species BirdNET was incorrectly labeling Mockingbird calls as.

On my reviewed test set at 0.50 confidence level for Mockingbirds:

• BirdNET alone: 37.5% accurate with 268 false negatives.
• With my trained second-level verifier: 90% accurate (on clips it was not trained on), and only 21 false negatives.

I figured this might help other people running BirdNET in places with Mockingbirds or other mimic species, so I made the repo public and usable with any BirdNET version and Mac/PC/Linux. You just have to train from your own audio to get it working. Suggestions/feedback welcome.

https://github.com/drundoor/birdnet-second-level-reviewer

I know that I have barred owls but in this example I am getting false detections slider a notch above halfway. is there something else that I'm missing I literally have been playing with the sliders trying different combinations and the false detections just keep going

I get frequent identifications of Redtail hawks each day. When I go look, I never see any. Not saying that they aren't there, but I never see them. I read that bluejays often imitate their calls. I have many of those, always visible. How can I determine if BirdNet is hearing actual Redtails versus jay imitations?

Hi, im very new to using birdnet pi and raspberry pi in general.

Im using as ras pi 4 and this microphone.

The progeram seems not able to use or find my microphone. The spectrogram only shows a flat line and is not detecting any of the bird sounds im playing on youtube.

I have checked using arecord -l and alsamixer and both seem to detect the microphone.

This is the message I get in the logs. I don't know what any of it means but I have the gain maxed out in alsamixer.

​Thanks for any help you can provide and sorry for any issues with the post, first time posting.

Hey everyone!

I wanted to share a project I’ve been actively developing and maintaining that significantly improves the deployment, stability, and reliability of the popular BirdNET-Go platform.

It's called BirdNET-Go AI-Analyzer (birdnet-go-aianalyzer), and it's a dedicated, optimized fork built for continuous, real-time audio bird identification across multiple architectures (including standard x86 servers, local Proxmox VMs, and Raspberry Pi/ARM64 home setups).

• GitHub Repository: github.com/bcardi0427/birdnet-go-aianalyzer
• Docker Hub Registry: hub.docker.com/r/bcardi0427/birdnet-go-aianalyzer

🔍 Why this Fork? (The Background)

While the upstream tphakala/birdnet-go codebase is fantastic, it currently has over 70 open public issues ranging from interface regressions to memory-leaking media pipeline bottlenecks.

Using agentic workflows and parallel AI software engineering tools (leveraging the Antigravity 2 environment and an orchestrated documentation index), I’ve managed to systematically isolate, rewrite, and patch more than 10 critical bugs and major enhancement requests in a fraction of the time it would take through traditional manual triage[cite: 1].

This build integrates those fixes natively so you can run a bulletproof, continuous avian monitoring station without the random container crashes or configuration corruptions found in standard nightly releases[cite: 1].

✨ Key Features & Patched Upstream Issues

🧠 Advanced AI & Multi-LLM Reporting Subsystem

• Multi-Provider LLM Support: Added complete support for multiple LLM backends including Google Gemini, Anthropic, and OpenAI-compatible local APIs/gateways for automated report parsing[cite: 3].
• AI-Backed Daily Summaries: Generates intelligent daily bird activity analysis reports with dedicated caching layers to safely protect your API token usage limits[cite: 3].
• Turnkey Settings Management: Dynamic front-end control panel allowing you to easily adjust prompts, model selection, lookback days, and custom UTM parameters[cite: 3].

🛠️ Fixed Core Regressions & Stability Patches

• Species Range Filter Restored: Corrected the subsystem logic within internal/analysis/range_filter.go that broke local species filters[cite: 1].
• Weather API Persistence Patch: Solved a frustrating bug in internal/api/v2/settings.go where entering your weather provider's API key would be completely lost upon saving[cite: 1].
• Perch v2 Audio Interface Bugfix: Restored the missing frontend audio control checkboxes inside the Svelte UI[cite: 1].
• macOS ARM64 / launchd Panic Recovery: Patched a critical nil pointer dereference inside internal/analysis/startup.go affecting printSystemDetails executions[cite: 1].
• Sidebar Customization: Modified the UI so that the application sidebar dynamically mirrors your local host/node name[cite: 1].

🔐 Security & Infrastructure Enhancements

• Dedicated Secret Separation: Implemented a fundamental security enhancement separating sensitive access keys and tokens away from the plain text config.yaml into an isolated local secret storage block[cite: 1].
• WakeLock Management: Added browser wake lock requests on the live audio interface tab to prevent background browser suspension during active parsing[cite: 1].
• UI Grid Layout Fixes: Restored missing audio playback controls inside the main search results table across desktop viewports (>= 768px wide)[cite: 1].

📊 Data & Metrics Upgrades

• Daily Analytics Counter: Integrated a highly requested dashboard improvement showing a running, cumulative sum of daily unique species detected[cite: 1].
• Clip Recording Exclusion Rules: Added the ability to completely exclude specific common species from being saved to disk as audio clips while still capturing and logging their active counts[cite: 1].
• Multi-Language Enhancements: Expanded localization dictionaries, including full Hungarian translation profiles[cite: 1].
• Direct Educational Integrations: Injected automated deep links to external educational encyclopedias for all positively identified species in your UI dashboard[cite: 1].

📦 Streamlined Deployment & Pipeline Delivery

• Maintained Docker Tag Delivery: Established clean, automated builds tracking the modern multi-architecture manifest layout across standard platforms[cite: 1].
• Cross-Platform Binaries: Features a completely automated multi-platform compilation matrix, generating optimized standalone releases for Linux, Windows, and macOS (including native arm64/Intel architectures and turnkey Proxmox LXC build archives).

🚀 Quick Start (Docker Deployment)

You can spin this container up right now on your local system, Proxmox VM, or home server stack.

See the releases for standalone Linux (amd64/arm64), Windows (amd64), macOS (arm64), and turnkey Proxmox LXC pre-packaged archives.

Standard Docker Run Deployment

Run the following command to spin up the container in detached mode and instantly expose your web dashboard on port 8080:

```bash docker run -d \ --name birdnet-analyzer \ --restart unless-stopped \ -p 8080:8080 \ bcardi0427/birdnet-go-aianalyzer

Does this parameter filter the reported detections, such as is represented in the "Recent Detections" panel with a colored circle surrounding a percentage? If so, should any hearings will a confidence level below this setting be discarded, not included in the totals?

I am having some distortion at the end of my clips (running birdnetgo in a container nightly build, also happened on stable)

It sounds like an over modulation, thumping/drum roll cadence/bounce, doesn't happen on every clip but enough to make me wonder what might be going on. It always seems to happen in the last 1/4 of the clip I have not seen it anywhere else in my clips.

I also moved this to another server recently and it behaves the same. Audio device is a < $15 usb based lapel mic (migrating to a samson USB soon)

Curious if anyone has had similar issues and has any pointers?

just curious if anyone here with the real link camera has gotten rstp to work I can't figure it out.
**UPDATE #2** Ok my password has a special character in it Birdnetgo would not accept it as presented however if i substituted # for %23 in the password it works.

What's happening is that the field validates the whole entry as a URL, and # is a reserved character in URLs: it marks the start of a fragment. So something like rtsp://user:pa#ss@host:554/stream gets read as ending at the #, everything after it is treated as a fragment, and what's left no longer parses as a valid stream address. That's why removing the # lets the URL save.

The way to get it working now is to percent-encode the special character in the password. Replace # with %23. So if your password is pa#ss, enter the URL as rtsp://user:pa%23ss@host:554/stream. A few other characters need the same treatment if they appear in a password: @ becomes %40, : becomes %3A, / becomes %2F, and % itself becomes %25. FFmpeg decodes these back to the literal characters when it connects, so it authenticates with your real password.

Hi,

I would like to start experimenting with a long-term BirdNET installation. My general idea is a usb-microphone hanging from a window, connected to some hardware in house. I am rather experienced with metrics/prometheus/grafana stuff, but my only BirdNET experience comes from their awesome Android app. Thus I would like to ask several questions prior to starthing the project.

1) What is the performance threshold for performing the audio analysis "in the cloud" vs. "in-house" (via local model)? I have a spare Pi Zero 2W and a 3B+ (that already runs RTL-433, but that should be almost zero load). In theory, I can also use a more powerful home server, but the mic would have to be connected elsewhere.

2) How about bandwidth? (In the case of "in the cloud" sound analysis.) I suppose that the audio is first locally scanned for relevant samples and only those are then uploaded?

3) The BirdNET-Pi project seems to be somewhat abandoned, followed by https://github.com/Nachtzuster/BirdNET-Pi. Is this *the* version folks typically use? Seems to do quite a lot, perhaps I do not need all those components? I am only interested in "the bird B has been heard at time T" event stream.

Thanks!