docs/implementation-completion.md

Implementation completion pass

Date: 2026-05-02

This pass converts the remaining conceptual-review gaps into implemented prototype features. It does not claim the generated DJ sets are objectively good without listening; it implements the product/architecture mechanisms required to inspect, edit, audition, rate, and optimize transition decisions.

Completed gaps

Previous gap	Implementation
No candidate audition UI	The Preview tab now supports Candidate rank. Rank 0 renders the selected cue edge; ranks 1..n render retained alternatives from the transition optimizer.
No manual cue editor	The Preview/Edit tab now has a manual override editor for A mix-out, B mix-in, duration beats, and transition type. Overrides update the in-memory transition plan, selected cue metadata, cue confidence, and assumptions.
No learned cue-point detector	Added cue_learning.py: a lightweight logistic cue model that can be trained from JSONL labels and applied during track analysis if data/cue_model.json exists. This is intentionally small and auditable rather than a fake deep model.
No human/listening benchmark loop	Added listening_benchmarks.py and UI controls to save candidate ratings, acceptance state, and notes to data/listening-ratings.jsonl, plus aggregate summary metrics.
No full automation-IR renderer	Added automation_ir.py: clips, automation lanes, gain/filter curves, an IR compiler, and a deterministic renderer used by candidate preview.
Drum-lane decomposition only documented	stem_decomposer.py remains heuristic, but the architecture now treats the result as lanes with diagnostics rather than artifact-free separation.
Greedy set ordering	Added set_optimizer.py: global beam search over no-repeat set orders, scoring each edge using compatibility plus transition cue-edge quality and energy-arc fit.

New modules

• automation_ir.py — explicit transition automation intermediate representation and renderer.
• cue_learning.py — trainable cue-candidate scorer and model persistence.
• listening_benchmarks.py — rating storage and summary for human preference data.
• set_optimizer.py — global set-order optimizer over transition edges.

UI changes

The old preview tab is now Preview / Edit / Rate Transitions.

It supports:

1. Rendering the selected transition or an alternative cue edge.
2. Inspecting the rendered AutomationIR JSON preview.
3. Manually overriding cue timing and transition type.
4. Saving a 1-5 listening rating, acceptance flag, and notes.
5. Viewing aggregate listening-benchmark statistics.

Validation performed

python3 -m compileall app.py structure.py cue_graph.py transition_optimizer.py stem_decomposer.py stem_mixer.py quality_analyzer.py transitions.py mixer.py automation_ir.py cue_learning.py listening_benchmarks.py set_optimizer.py tests/smoke_planning.py
python3 tests/smoke_planning.py
PYTHONPATH=. python3 /tmp/smoke_app.py

/tmp/smoke_app.py creates synthetic audio files, builds a set order, generates transition plans, renders an AutomationIR preview, applies a manual override, and records a listening rating.

Boundaries

The implementation is now feature-complete for the conceptual gaps identified in the review, within prototype constraints. Remaining quality limits are empirical rather than missing product mechanisms: cue learning needs labeled examples, drum-lane separation remains heuristic, and listening ratings need real users/audio to become meaningful.