Acoustic Atlas: An Offline Open-Weight Model That Finds the Best-Sounding Places to Touch Grass

A developer built Acoustic Atlas, an offline-first Progressive Web App that uses Google's open-weight YAMNet audio classifier via TensorFlow.js to score how "alive" a location sounds. The app processes microphone audio in memory in roughly 0.96-second windows, classifies each second into nature and human-noise buckets, and stores only numeric Nature Scores and GPS coordinates in IndexedDB, never saving or uploading raw audio. It ranks a user's best-sounding spots and recommends the liveliest time of day, working in airplane mode after the first load.

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass https://dev.to/challenges/hacktoberfest-week1-2026-10-05 What I Built Acoustic Atlas is a listening companion that finds the places near you that sound alive. Most outdoor apps tell you where to go. This one measures what a place actually sounds like. You tap "Listen" at a spot, put your phone in your pocket, and walk or sit for a few minutes. An open-weight audio model classifies the soundscape on-device, separating what it hears into nature sound birdsong, wind, water, insects, rustling leaves and human noise traffic, engines, horns, construction, crowds . When the timer ends, your phone vibrates and gives you a Nature Score for that spot and time of day. Over a few walks it builds your personal atlas: - Your best-sounding spots, ranked - The best time of day for each spot for example, "dawn is 3x livelier than evening" - A "quietest, most alive place near me right now" suggestion The screen is the shortest part of the experience. The app is only used to start and stop a listen, and all the real time is spent outside, listening. It's for walkers, runners, birders, people who live in noisy cities, and anyone who wants a reason to sit somewhere green and pay attention. Code 🌿 Acoustic Atlas Acoustic Atlas is a static, offline-first Progressive Web App PWA that uses an on-device TensorFlow.js Machine Learning model YAMNet to score how "alive" a natural location sounds, building a personal atlas of the best-sounding spots and times. 🎯 Core Philosophy & Privacy Model The screen is designed to be the shortest part of the experience: 1. Tap Start Listening pick 3, 5, or 10 minutes . 2. Put the phone in your pocket and listen outdoors. 3. Get a Nature Score 0–100 , top sound breakdown, birdsong share, and optimal time-of-day recommendation. Zero Audio Uploads & 100% On-Device - In-Memory Streaming : Microphone audio is resampled to 16 kHz mono in RAM, sliced into ~0.96s windows 15,360 samples , evaluated by YAMNet, and immediately discarded . - No Audio Saved or Uploaded : Raw audio is NEVER stored on disk or transmitted over any network. Only numeric score aggregates are stored locally. - … How I Built It - Model: YAMNet, Google's open-weight audio event classifier Apache 2.0 , trained on AudioSet and able to recognize 521 sound classes. It runs fully in the browser through TensorFlow.js, with the model files bundled locally instead of fetched from a CDN. - Scoring: Each second of audio is classified, and the class probabilities are grouped into "nature" and "human noise" buckets. The Nature Score is the share of the soundscape that is nature. The class grouping is a plain config file, so anyone can change what counts as nature for their region. - App: A static Progressive Web App. A service worker caches the app and the model, so after the first load it works in airplane mode on a trail. - Storage: Spots, scores, time of day, and GPS coordinates are saved in IndexedDB on the device. There is no backend and no account. - Screen-minimal design: After you tap Listen, the screen drops to a dark minimal card with a countdown. The phone vibrates when done, and you only look at the screen again for the result. - Audio is never saved: Audio is processed in memory in short windows and thrown away. Only the numeric scores are stored. Why Does Open Innovation Matter? - Privacy is the product. An ambient soundscape recording can capture conversations, your family, and your location habits. With an open-weight model running on-device, the audio never leaves your phone and only anonymous scores are kept. A cloud audio API would need the recordings uploaded, which is a bad fit for something you carry everywhere. - It works where the signal doesn't. The best-sounding places parks, hills, riverbanks often have the worst reception. Local inference makes zero connectivity the normal case, not an edge case. - It's free to run. There are no per-minute audio API costs, so you can listen for hours without paying anything. - You can change what "nature" means. A forest in Canada and a riverside in Southeast Asia sound very different. Because the model and the class mapping are open, anyone can adjust the scoring for their region, or swap in a bird-specific model like BirdNET or a fine-tuned one. - It's auditable. Anyone can read exactly how a score is computed. There is no black-box "wellness score."