Pi Pico Ultrasonic Schlieren Turns Home Sound Mapping Tiny

A DIY audio engineer is replacing the usual “thousands of points” room-mapping grind with a Schlieren imaging setup—then solving Schlieren’s audio-frequency limits by shifting the sound model into ultrasound. Using a Pi Pico, a Pico 2W, and a turntable-based a

The problem starts the way most audio engineering dreams do: with a map that’s too big to finish.

If you want to model where sound goes inside a living room. you can set up a listening area and use a microphone to sample the space. But doing it the traditional way means thousands of points—and that kind of tedium is hard to romanticize when you’re trying to make spatial audio actually work in real homes.

So [PlasmatronX] went looking for a different kind of visibility. Instead of measuring sound the normal way, he turned to Schlieren imaging, the optical technique that makes density changes visible. The catch is that Schlieren imaging struggles with audio frequencies. and imaging an entire living room at those ranges is a bigger challenge than the software ever will be.

His workaround is as clever as it is a little surreal: he scales everything down by shifting to ultrasonic frequencies. In his setup, the room’s acoustic behavior is visualized while the sound waves are treated as ultrasound—small enough to image, and fast enough to analyze.

At the core is the mirror-and-razor Schlieren arrangement with an 8″ telescope mirror. Within the circular imaging area, he builds what he calls a CAT—Computer Acoustic Tomography—array. It’s a rig mounted on a turntable that lets him place ultrasonic transducers to match different speaker setups. then rotate so he can see what the scaled-down waves are doing from multiple angles.

Because the waves aren’t going to freeze in place for the camera. he adds a stroboscope to help capture the moving ultrasonic signals. The ultrasound itself is generated by a Pi Pico. In the frequency domain, the signals are scaled 4:1: a tone that would be a high 10kHz whine becomes inaudible 40kHz. From there, the signals pass through a DIY 8-channel amp into both ultrasonic transducers and larger “cat-repellent speakers” from AliExpress.

Controlling all of it takes a small stack of single-board hardware. The microcontroller is actually a Pico 2W, with its “W” used to communicate over Bluetooth to a Pi 4. That Pi 4 handles the camera. the stepper motor for the turntable. image processing. and it also provides the timing for the audio signals.

With the system running. the workflow is straightforward in concept: set up a scaled-down 7.1 surround configuration and a tiny soundbar. then test how the steered beams look compared with a fuller surround arrangement. The video shows a clear gap—beam steering from the small soundbar stands out against the “true surround” visualization.

How that difference translates into listening pleasure is where the certainty drops. [PlasmatronX] is explicit that perception would still be subjective. But one part of the results feels harder to argue with: the role of soft furnishings.

In his tests, he doesn’t spend time matching the exact material properties of real curtains. Instead, he focuses on what happens when soft furnishings are included without the painstaking material scaling. At 5kHz or 20kHz, the effect is to deaden sound. In the visualization. you can see it—and the impact is shown as much bigger for the shaped. steered beams of the soundbar than it is for surround sound.

By the end, he decides to stick to headphones. It’s a pragmatic conclusion that lands with a familiar kind of disappointment: even if you can make the physics visible, the leap from “what looks right” to “what feels better” still doesn’t automatically happen.

What makes the project worth watching, though, isn’t the final preference—it’s the method. He’s shared the code on GitHub for anyone who wants to try scaling acoustic models with ultrasound Schlieren imaging.

The video is embedded below: https://www.youtube.com/watch?v=_VQDn4HWRM8.

Schlieren imaging Pi Pico Pico 2W Pi 4 ultrasonic transducers acoustic tomography spatial audio 7.1 surround soundbar beam steering GitHub code