Markov Chains for Generative MIDI with Codex + Logic Pro

Editorial diagram showing a MIDI clip becoming new musical ideas through a Markov transition map and Logic Pro
From a MIDI clip to a transition map, a sampled continuation, and a Logic Pro sketch.

Your favorite MIDI clip can become a little machine for new ideas.

A Markov chain models a sequence by learning which musical event tends to come next. In a pitch model, a first-order chain only looks at the current note. A higher-order chain remembers the last few musical events too. More memory can sound more familiar, but with small data it can also repeat itself.

Choose the musical state

The result depends on what you decide to model:

  • pitch or pitch class for melodic contour;
  • pitch plus duration for more realistic phrasing;
  • rhythm or inter-onset intervals for groove;
  • chords or pitch-class sets for harmonic motion;
  • velocity bins for dynamics; or
  • a simplified shared state for multi-track material.

The practical Codex workflow

  1. Give Codex MIDI examples you made or have permission to use.
  2. Read the notes with an open-source MIDI library such as mido.
  3. Turn the events into states such as pitch plus duration.
  4. Count context-to-next-state transitions and normalize them into a transparent probability map.
  5. Generate several candidates with constraints for key, scale, range, bar count, and leap size.
  6. Export the strongest sketch as a standard .mid file.

Where PyTorch fits

The map does not need to be a black-box neural network. Python counters and NumPy can build the transition matrix. PyTorch can hold the matrix, mask illegal musical choices, apply temperature, and sample the next state. Lower temperature stays closer to the learned material; higher temperature explores more unusual continuations.

Bring the sketch into Logic Pro

Import the MIDI region, then quantize, humanize, add intentional variation, or inspect the harmony. Logic can help you audition the idea with chord-aware instruments and Session Players, but arrangement decisions still belong to the producer.

What it does—and does not—promise

Markov chains are fast, transparent, and useful with small amounts of data. They can wander, repeat, or get stuck, especially at higher orders. A basic chain generates local continuation ideas; it does not automatically create a verse, chorus, or complete arrangement.

Use source MIDI you authored or have permission to use, generate a few candidates, and keep the moments that earn their place by ear.

Explore more DSIV Music production guides and original music at dsivmusic.com.