Trail Ears: Identify Bird Calls in Your Browser, Even With No Signal

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass

What I Built

Trail Ears is a website that identifies bird calls. You record or upload a short clip, and it shows the top 3 likely species with confidence scores. You can log each sighting with a note and export your trail log as a CSV.

It is built to make the screen the shortest part of a walk: It keeps screen time short: record a clip of up to 12 seconds, put the phone away, and check the result and log it when you’re ready. It works for hikers, birders, run clubs, or anyone who has stood outside wondering what that sound is.

Demo

Live site: https://Er-Simran-Jaiswal.github.io/birdnet-web/trail-ears.html

Code

Repo: https://github.com/Er-Simran-Jaiswal/birdnet-web

The new pieces are trail-ears.html (the interface, location filter, and trail log) and sw.js (the offline cache).

How I Built It

• Model: BirdNET, an open-source bird sound identification model from the Cornell Lab of Ornithology and Chemnitz University of Technology.
• Inference: it runs in the browser with TensorFlow.js, using the open-source browser port georg95/birdnet-web, which I forked.
• My part: a new front end with recording and upload, top-3 results, a minimum-confidence slider, an optional location filter that narrows species to the area and time of year, and a local trail log with CSV export.
• Offline: a service worker caches the model and page after the first load, so it can keep working with no signal.

Why Does Open Innovation Matter?

• No signal needed: a closed API stops working the moment you lose coverage, which on a trail is almost always. With open weights the model lives in the browser's cache.
• Privacy: audio and location are processed on the device, so there is no server to send them to.
• Cost: no API key, no account, no usage bill.
• Hackable: because the model and the browser port are open, I could build a new interface and location filtering on top without asking anyone for permission.

Credits: BirdNET (CC BY-NC-SA 4.0, non-commercial use) and the TensorFlow.js browser port by georg95.