Fugleramme turns local bird detections into an illustrated page on an e-paper frame. BirdNET-Go listens for and identifies birds; Fugleramme reads those results through BirdNET-Go’s API, chooses which species to show, arranges their illustrations, and updates the display when the page’s content changes. It is an ambient field guide, not a live dashboard: the screen can take tens of seconds to refresh and is designed to avoid repainting when there is nothing new to show.
How Fugleramme turns detections into a field guide
The system separates listening from presentation. BirdNET-Go handles microphone access, acoustic inference, confidence thresholds, geographic filtering, audio clips, and its detection database. Fugleramme requests recent detections and species summaries through BirdNET-Go’s /api/v2 HTTP interface, then prepares a page for the frame. The project author says the detector can run on the same Raspberry Pi, on another machine on the home network, or on an existing BirdNET-Go station.
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- Listen and identify: BirdNET-Go processes audio and records its detections according to its own configuration.
- Select and normalize: Fugleramme filters out non-bird classes and normalizes scientific names when taxonomy changes.
- Compose the page: it chooses available illustrations, sizes the bird silhouettes, reserves room for labels, and packs the images into a layout.
- Render for the destination: the web kiosk and physical panel use consistent placement; the e-paper path converts the image to the panel’s six-color palette with dithering.
- Refresh only when needed: Fugleramme compares the current page-affecting state with the previous one before requesting a display update.
The project overview says the default BirdNET-Go model it describes covers more than 6,500 bird species and can run continuously on a Raspberry Pi. That is a project-reported description of the model version discussed in the September 16, 2026 overview, not a timeless specification. Read the Fugleramme project overview.
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What appears on the page—and what does not
Fugleramme uses historical natural-history illustrations rather than uniform species cards. Its technical write-up reports more than 800 cut-outs representing over 400 species. The source plates favor Scandinavian, British, and central European birds, so the illustration collection is strongest for those regions rather than universal. Each asset has source and license information in a style manifest.
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Bird silhouettes are sized using species identity and body-mass data, and the layout reserves space for labels while packing irregular shapes. If the collection lacks an illustration for a detected bird, Fugleramme omits it rather than substituting a fabricated or unrelated image. As a result, a detection may not appear in the illustration page even when BirdNET-Go has identified it.
Why the screen does not update with every poll
The technical article says Fugleramme polls for data every five seconds. Polling does not mean the display is refreshed every five seconds: the service derives a state key from selected species and other settings that affect the page. If the key has not changed, it skips rendering and panel refresh. An optional minimum interval can also limit activity-driven updates, while configuration changes can take effect immediately.
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This matters because e-paper is slow by conventional interface standards. The technical write-up reports a 12-second core refresh at warm operating temperatures and roughly 20–35 seconds for a complete update, including data transfer and ready-state handling. These are figures reported in the project article for the named display, not independent measurements or a guarantee for every setup. If the detector is unavailable, Fugleramme preserves the last good page; if a panel update fails, it keeps the rendered image and retries. An empty detection result is treated differently from an API outage: no birds can produce a bare perch, while an outage should not erase the last known page.
Choose a full detector build or a display-only frame
The two configurations differ mainly in whether the frame also hosts the audio detector. The project does not report controlled cost, reliability, or recognition-accuracy comparisons, so these are build choices rather than performance rankings.
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| Configuration | Detector placement | Frame hardware | Best fit |
|---|---|---|---|
| Full build | BirdNET-Go runs alongside Fugleramme | Raspberry Pi 5 with 2 GB RAM, storage, active cooling, USB audio input, 13.3-inch Inky panel, and A4 frame | A new installation that needs both detection and a physical display |
| Display-only | Fugleramme queries an existing BirdNET-Go host on the home network | Raspberry Pi 4 or Pi Zero 2 W can drive the display; the project author says the Zero renders more slowly | Adding a frame to an existing detector station |
| Web-only | Uses BirdNET-Go detection data | No e-paper hardware required | Trying the composition as a web or kiosk display |
The full-build hardware list and display-only options come from the project overview. The technical article identifies the recommended panel as Pimoroni’s 13.3-inch Inky Impression, A4-sized and 1600×1200, using Spectra 6 colors: red, green, blue, yellow, black, and white. Its reported refresh timing should be checked against current manufacturer documentation before being treated as a current product specification.
For continuous inference, the project author emphasizes cooling and reliable power. The article also says microphone quality matters more to recognition than adding memory. If an existing BirdNET-Go machine already handles audio, separating it from the frame avoids duplicating that listening hardware; Fugleramme’s basic frame function is described as working over the local network without a cloud account.
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Plan the frame and ventilation
The enclosure affects both the visible image and the electronics. The project overview advises cutting the mat so it covers the bezel without obscuring the image, leaving the back open or providing a large ventilation cutout, and handling the glass panel by its edges. These practical details matter especially in a setup expected to run continuously: a tightly sealed frame can impede airflow around the hardware.
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All performance figures below are reported by the project author, not independently tested. The DEV Community technical article attributes about 90% of a render to the packing stage and says a cached second path reduced from 5.4 seconds to 0.5 seconds in the project’s measurements. Those figures describe the author’s rendering implementation and should not be read as guaranteed timing for other hardware or datasets.
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- Model coverage: more than 6,500 bird species in the default BirdNET-Go model described in the September 16, 2026 project overview.
- Illustration library: more than 800 cut-outs for over 400 species, as reported in the technical article.
- Panel timing: 12 seconds for core refresh at warm operating temperatures and roughly 20–35 seconds for a complete update, as reported in the technical article.
- Rendering: about 90% attributed to packing; cached second-path time reported as falling from 5.4 seconds to 0.5 seconds.
Privacy and the boundary between the two services
The project overview describes a local setup and quotes its author, Arne Giacomo Munthe-Kaas: “Nothing is uploaded for classification.” The statement applies to the described local classification arrangement; BirdNET-Go and the local configuration still determine how detection data and audio clips are handled. BirdNET-Go owns the detection database and may store clips, so the quote should not be taken as a universal privacy guarantee for every deployment.
What makes the project different from a dashboard
Fugleramme’s central design choice is to make the page change when its meaning changes, not whenever the data endpoint is checked. That suits a slow, persistent e-paper surface: a bird’s presence can become an illustration to glance at across a room, while a network failure leaves the last useful page in place. The trade-off is that it is not a real-time activity monitor, and the visible species are constrained by both detection output and illustration availability.
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