TriuneCadence

TriuneCadence is a Python implementation of a modular neural music-composition system inspired by Wesley R. Elsberry's 1989 master's thesis on constrained melodic composition.

It combines three different network families in one pipeline:

• a Hopfield-Tank note generator
• a back-propagation critic (Salieri)
• an ART1 novelty/category module (Beethoven)

The repository includes:

• a modern Python codebase with generic network modules and thesis-specific adapters
• legacy thesis source, text, and data files in THES/
• timing, entropy, and predictability analysis for generated note sequences
• JSON serialization for learned model state and run reports

Why This Repo Exists

The original system was implemented in Turbo Pascal on late-1980s hardware under severe memory constraints. That led to pointer-heavy data structures and implementation complexity that obscured what was, architecturally, a strong multi-network design.

This repository keeps the core ideas accessible:

• generic reusable implementations of the underlying network families
• a thesis-faithful composition pipeline built on top of those generic modules
• a practical environment for experimentation, analysis, and historical comparison

Quick Start

Run a short composition from the thesis data:

python -m composer_ans --thes-root THES --notes 16

Or, if installed as a package:

triune-cadence --thes-root THES --notes 16

Save model state and a run report:

triune-cadence \
  --thes-root THES \
  --notes 32 \
  --save-salieri salieri.json \
  --save-beethoven beethoven.json \
  --save-report run.json

Sweepable Parameters

The CLI currently exposes a few parameters that are useful for experiments:

• --object-threshold
• --max-attempts-per-note
• --art-vigilance
• --art-vigilance-decay

Saved run reports include those parameters along with:

• note sequence
• per-note generation time
• total runtime
• unigram entropy
• first-order conditional entropy
• normalized entropy
• predictability
• redundancy

Project Layout

Core Python modules live in composer_ans/:

• generic Hopfield-Tank core: composer_ans/hopfield.py
• generic back-propagation core: composer_ans/backprop.py
• generic ART1 core: composer_ans/art1.py
• thesis-specific wrappers: composer_ans/salieri.py, composer_ans/beethoven.py
• integrated composition pipeline: composer_ans/pipeline.py
• analysis and reporting: composer_ans/analysis.py, composer_ans/reporting.py

Legacy materials are in THES/.

Historical Context

The thesis reports that the integrated system generated 152 notes in about three hours on a 16 MHz 80386-class machine, and in about fifteen hours on an 8088-based machine with an 8087 coprocessor. This Python version can report per-note generation time directly so present-day runs can be compared against those historical figures.

Development

Run the test suite with:

pytest -q

Related Repo Notes

The original migration planning artifact is preserved in MIGRATION_PLAN.md.