A MIDI step sequencer built on a Raspberry Pi Pico, with an OLED display, a rotary encoder, and an 8×8 NeoPixel matrix used as the step grid.

Besides manually programmed tracks, it supports a set of generative sequencing strategies — Euclidean rhythms, L-systems, cellular automata, Markov chains, arpeggios, call-and-response, drones, and more — each one turning a track into an algorithm instead of a fixed pattern. Scale/root-note awareness, adjustable mutation, and chord-note support keep the generated parts musical. Multiple tracks can run different strategies and step counts at once for polyrhythms.

Sends MIDI notes over a hardware serial DIN port, with optional MIDI input via an optocoupler. Can run tethered via USB or portably off a LiPo battery and a 5V booster board.

To make this extra fun, I repurposed a kids’ toy briefcase and made it a bit punk.

Status: Finished.

Project page: https://git.dejvino.cz/dejvino/PicoWaveTracker

A pocket-sized modular synthesizer built around a Raspberry Pi Pico (RP2040), with all patching done on a 12×12 grid instead of physical cables.

Oscillators, filters, envelopes, and a handful of other modules get placed onto the grid and wired together however you like, so the same box can become a DX7-style FM patch one moment and a slow, drifting drone the next. Audio streams out over I2S, with the synthesis math running in fixed-point to keep the Pico’s core from choking under a full patch. A rotary encoder, a volume potentiometer, and a 128×64 OLED handle all the interaction, and a 3.5mm TRS-A MIDI input lets it take notes from an external keyboard or sequencer. It runs off a LiPo cell and a booster board, small and portable enough to carry around.

A desktop simulator ships alongside the hardware, letting patches be designed and auditioned before ever touching the device, with patches transferable both ways. It comes with eight demo patches plus a few built-in presets that mimic the DX7, and saves patches to EEPROM so nothing is lost when it’s powered off.

Status: Finished.

Project page: https://git.dejvino.cz/dejvino/NoiceSynth

My Master Thesis software suite. The aim was to create a high-performance correlation computation software + visualisation framework for large datasets.

vizualizaceMultipleSignalsMap

Corrputer

Correlation computation software. Written in C++ with focus on high performance, uses MPI for running on any kind of setup, from one to many computers.

Source code: git.dejvino.cz/masterserver

Visputer

Data visualisation framework in Python built on top of PyQtGraph. It interconnects with Corrputer quite nicely.

Source code: git.dejvino.cz/mastervis