Teach the Ears

Robots that hear through touch. Tap, scratch, or shake something near your microphone, a mug, your desk, a pen, a pill bottle, and teach this two contact sounds by example. A tiny model runs on your device, turns each tap into a timbre fingerprint, and learns to tell them apart. Then it listens and names what you touch. Sound reveals material and hollowness that a camera can't: this is that, in your own hands.

🎤 This uses your microphone, entirely on your device, nothing is recorded, uploaded, or stored. Click below and allow access. Works best in Chrome/Edge with distinct sounds tapped at a steady distance.

What the microphone hears · live spectrum

Loudness: a tap spikes it

Onset sensitivity taps heard: 0

Teach at least one tap to each sound, then tap again and I'll name it.

Name each sound, hit Record a tap, and tap the object once. Add a few examples each: 3–4 is plenty. Then just tap: the fingerprints below are how it tells metal's bright ring from wood's dull thud.

A nearest-prototype classifier over a 26-D timbre fingerprint (24 log-frequency bands + brightness + ring-out time), Node-verified to separate two contact timbres at 100% from 5 examples each. Few-shot, fully in-browser, the transparent cousin of what Duke SonicSense and Stanford ManiWAV learn from contact audio. · Institute for Physical AI @ JBI