u/therealPaulPlay

Hey,

I've spend the past few days building a rule-based library (in Go) to recognize alerts in audio. That includes police car sirens, air sirens, ambulance sirens, emergency signals, horns, house alarms and smoke detector alarms.

It uses a set of ~200 sound files that I pulled from various sources (Freesound, Pixabay Sounds, YouTube..) and trimmed & edited in Audacity.

Instead of training an ML model, because I needed something that runs comfortably on a microprocessor, I went with a rule-based approach where an optimizer script automatically computes the most "optimal" ruleset that works for all included sound files and clearly separates positives from negatives.

It does so based on audio metrics (Disclaimer: I let an LLM generate the metric calculation code) that portray different aspects of the sounds.

I've repeatedly tested with sound files that aren't in the training data to test against overfitting. I'm also augmenting the training data by overlaying ambience sounds at lower volume, adjusting playback speed, adjusting volume, and automated trimming. That produces a couple thousand samples.

Repo: https://github.com/therealPaulPlay/go-alert-detector

Audio classification model for detecting alerts (sirens, alarms - such as police car sirens, security alarms, air raid sirens..)

Library for detecting audio alerts (alarms, sirens, emergency signals..), written in Go