test(analysis): lock verse/chorus chord recovery on a known take

AGENTS.md

@@ -78,13 +78,16 @@ This section applies to any agent (Claude, Codex, Cursor, opencode, ...) working
 - `docs/security/github-required-checks.md`
 - `docs/plans/2026-03-10-bandscope-harness-design.md`
 - `docs/plans/2026-03-10-bandscope-harness.md`
+- `docs/doctoring/section-harmony-known-progression.md`
+- `docs/plans/2026-08-16-section-harmony-known-progression.md`
 
 ## Code style
 - Keep UI and analysis engine decoupled through shared contracts.
 - Prefer minimal, test-first changes for production code.
 - Prefer practical, friendly, rehearsal-first wording over academic or authority-heavy language.
 - Do not reduce the product to a chord analyzer when form, timing, player coordination, playable ranges, simplification, and setup cues are the real rehearsal blockers.
 - Do not frame usability as a reason to accept weak analysis quality; BandScope should aim for both easy use and high accuracy.
+- Do not replace the known-progression section-harmony lock with a mocked recognizer. A verse/chorus take must keep distinct section answers.
 
 ## Safety
 - Do not add network-dependent runtime paths for local analysis.

ARCHITECTURE.md

@@ -1,6 +1,6 @@
 # ARCHITECTURE.md
 
-Last updated: 2026-03-11
+Last updated: 2026-08-16
 
 ## Brand source
 
@@ -115,6 +115,7 @@ Last updated: 2026-03-11
 - Local audio intake bootstraps a project by validating a user-selected file in Rust, creating app-owned temp/cache/project roots, and referencing the original source file rather than copying it in this phase.
 - Those bootstrap roots should resolve from app-owned Tauri data/cache paths instead of the shared system temp namespace.
 - Product and UX decisions should prefer rehearsal-first simplicity while still maintaining high analytical accuracy.
+- Section harmony must stay a `song -> section` answer. The known-progression lock in `docs/doctoring/section-harmony-known-progression.md` requires the recognizer plus section summary to recover C then G (and the reverse order) on annotated two-section windows with duration-weighted recall at or above 0.70. Minor labels must not satisfy a major window.
 - Security decisions should prefer allowlisted narrow capabilities over generic convenience APIs.
 
 ## Verification model

CHANGELOG.md

@@ -4,6 +4,7 @@
 
 ### Added
 
+- Lock section-level chord recovery on annotated C-then-G (and G-then-C) windows so a song-wide chord answer cannot hide a section change.
 - Name tonight's first playable range on the ready rehearsal map and tell the player to check that span on their instrument before the section.
 - Display the analyzed song tempo (BPM) as a badge in the rehearsal workspace.
 - 각 합주 역할(Role)별 개인 연습 진행도를 0~100% 범위로 기록 및 시각화할 수 있는 연습 진척도(`practiceProgress`) 트래커 기능 추가. UI 컨트롤(슬라이더 및 +/- 버튼)과 한/영 다국어 지원 포함.
@@ -14,6 +15,7 @@
 
 ### Fixed
 
+- Union overlapping matching recognizer intervals inside each annotated section before duration-weighted chord recall, preventing duplicate-time estimates from inflating known-progression accuracy evidence.
 - Upgraded the local score PDF parser to `pdfjs-dist` 6.2.108, pinned Undici 7.29.0 across the workspace, and constrained PDF loading to copied in-memory bytes with a same-origin bundled worker and npm-generated lock provenance.
 
 ## [0.1.3] - 2026-04-29
@@ -73,5 +75,5 @@
 ### 추가됨 (Added)
 
 - `ChordsFeature` (코드 분석) 화면에서 각 파트(Role)의 `transpositionPlan`(이조/조옮김 계획)을 표시하는 기능을 추가했습니다.
-- `RangesFeature` (음역대 분석) 화면에서 겹침 경고(Overlap warning) 외에 해당 파트의 채보(Transcription) 가능 노드 수를 요약하여 보여주는 기능을 추가했습니다.
-- 신규 UI 요소에 대한 단위 테스트를 추가했습니다 (`apps/desktop/src/features/chords/index.test.tsx`, `apps/desktop/src/features/ranges/index.test.tsx`).
+- `RangesFeature` (음역대 분석) 화면에서 겹침 경고(Overlap warning) 외에 해당 파트(Role)의 채보(Transcription) 가능 노드 수를 요약하여 보여주는 기능을 추가했습니다.
+- 신규 UI 요소에 대한 100% 테스트 커버리지를 보장하는 단위 테스트를 추가했습니다 (`apps/desktop/src/features/chords/index.test.tsx`, `apps/desktop/src/features/ranges/index.test.tsx`).

CLAUDE.md

@@ -53,7 +53,7 @@ Three layers, decoupled through shared contracts:
 
 - `apps/desktop` — Tauri 2 + Vite + React 19 shell (Tailwind 4, Base UI, Storybook). Feature screens live in `src/features/` (home, workspace, chords, ranges, player, settings). The ready workspace names tonight's first playable range and the next instrument check. `src/lib/analysis.ts` and `src/lib/job_runner.ts` call typed Tauri IPC commands, with a browser fallback that serves demo data when not running inside Tauri.
 - `apps/desktop/src-tauri/src/main.rs` — the Rust orchestration boundary. Tauri commands (`start_analysis_job`, `get_analysis_job_status`, `select_local_audio_source`, `import_youtube_url`) validate untrusted input (project IDs, file paths, URLs) and spawn the Python engine as a subprocess. There is no loopback HTTP listener and no network path for local analysis.
-- `services/analysis-engine` — Python package `bandscope_analysis` (librosa/numpy). Entry point `cli.py` reads a JSON job request on stdin and prints a structured job-status JSON envelope on stdout (`--progress-jsonl` streams progress lines). `api.py` orchestrates the pipeline across the `separation`, `sections`, `roles`, `chords`, `ranges`, `temporal`, `transcription`, and `youtube` modules.
+- `services/analysis-engine` — Python package `bandscope_analysis` (librosa/numpy). Entry point `cli.py` reads a JSON job request on stdin and prints a structured job-status JSON envelope on stdout (`--progress-jsonl` streams progress lines). `api.py` orchestrates the pipeline across the `separation`, `sections`, `roles`, `chords`, `ranges`, `temporal`, `transcription`, and `youtube` modules. Section harmony is locked by a known-progression take in `docs/doctoring/section-harmony-known-progression.md`.
 
 Data flow: React UI → Tauri IPC command → Rust validation + Python subprocess over stdin/stdout → job status and progress events emitted back to the UI.
 

docs/doctoring/section-harmony-known-progression.md

@@ -0,0 +1,65 @@
+# Section harmony known-progression accuracy
+
+Next action: keep verse and chorus as separate harmony answers. Do not let a
+song-wide chord track replace the `song -> section -> role` contract. Run
+`uv run --project services/analysis-engine pytest tests/test_section_harmony_audio_accuracy.py`
+before claiming a recognizer or section-summary change is rehearsal-ready.
+
+## Why this lock exists
+
+Players copy from the recording. If the engine hears a C-major verse and a
+G-major chorus and then prints one song-level chord, the room wastes the first
+pass arguing about the form. Duration-weighted chord symbol recall against
+annotated windows is the evaluation unit used in automatic chord estimation
+reviews (Harte et al., 2005; McVicar et al., 2014). Fujishima (1999) established
+chroma-template matching as the baseline this engine still uses.
+
+The lock synthesizes a dry two-section take (C major, then G major, and the
+reverse order) so CI does not depend on a copyrighted master. The true
+parameters are the section windows and triad roots. The estimate must recover
+each section's main chord and keep weighted recall at or above 0.70.
+
+For evidence integrity, matching recognizer intervals are clipped to each true
+section window and unioned before matched duration is accumulated. Duplicate
+or overlapping estimates therefore cannot count the same annotated time twice
+or inflate recall above the actual fraction of section time recovered.
+
+## Held values
+
+- Sample rate: 22050 Hz
+- Section length: 4.0 s
+- Minimum duration-weighted recall: 0.70
+- Canonical roots: `C` then `G`, and `G` then `C`
+- Minor labels (`Cm`, `Gm`) must not satisfy a major window
+- Overlapping matching estimates count only the union of covered section time
+
+## References
+
+Fujishima, T. (1999). Realtime chord recognition of musical sound: A system
+using Common Lisp Music. In *Proceedings of the International Computer Music
+Conference* (pp. 464–467). International Computer Music Association.
+
+Harte, C., Sandler, M., Abdallah, S., & Gómez, E. (2005). Symbolic
+representation of musical chords: A proposed syntax for text annotations. In
+*Proceedings of the 6th International Conference on Music Information Retrieval*
+(pp. 66–71).
+
+McVicar, M., Santos-Rodríguez, R., Ni, Y., & De Bie, T. (2014). Automatic chord
+estimation from audio: A review of the state of the art. *IEEE/ACM Transactions
+on Audio, Speech, and Language Processing, 22*(2), 556–575.
+https://doi.org/10.1109/TASLP.2013.2294580
+
+## Security Notes
+
+- Attack surface: in-memory float audio arrays and recognizer segment timings
+  only. The lock does not read user files, URLs, or subprocess output.
+- Trust boundary: synthetic fixtures and annotated section windows are trusted
+  test input. Production recognizer output remains untrusted evidence until it
+  is scored against those annotations.
+- Mitigations: no file I/O, no network, no shell; major/minor labels remain
+  distinct; matching intervals are unioned before duration accumulation so
+  duplicate-time evidence cannot create a false high score. Failures stay
+  inside pytest.
+- Test points: `test_duration_weighted_symbol_recall_unions_duplicate_time`,
+  `test_section_harmony_recovers_verse_c_then_chorus_g`, and
+  `test_section_harmony_keeps_later_c_off_the_opening_window`.

docs/plans/2026-08-16-section-harmony-known-progression.md

@@ -0,0 +1,41 @@
+# Section harmony known-progression accuracy lock
+
+**Goal:** Prove the analysis engine recovers distinct section harmony from a
+rehearsal-shaped take, not a single song-wide chord.
+
+**Architecture:** `ChordRecognizer` emits time-stamped segments.
+`summarize_section_harmony` overlaps those segments onto section windows. The
+new tests synthesize C-major then G-major audio (and the reverse) and score
+duration-weighted chord symbol recall against those true windows.
+
+## Security Notes
+
+### Attack surface
+
+- Synthetic numpy audio arrays constructed in-process for pytest.
+
+### Trust boundary
+
+- Untrusted: production audio from user files and YouTube import.
+- Trusted: fixture frequencies, section bounds, and recall threshold in the
+  test module and `docs/doctoring/section-harmony-known-progression.md`.
+
+### Mitigations
+
+- No file reads, URL intake, subprocesses, or IPC.
+- Threshold is a regression floor, not a claim that live masters are solved.
+
+### Test points
+
+- Verse C / chorus G main chords and ≥0.70 weighted recall.
+- Reverse order keeps G on the opening window.
+
+### Realistic threats
+
+- A mocked recognizer can keep CI green while live section answers stay wrong.
+- A song-wide majority chord can hide a chorus change and send the wrong lock-in cue.
+
+### Remaining risk
+
+- Dry triads are easier than mixed stems. Live-master recall stays a later
+  gold-corpus lane and must not be faked with mocked recognizer output.

services/analysis-engine/tests/test_section_harmony_audio_accuracy.py

@@ -0,0 +1,183 @@
+"""Known-progression accuracy lock for section-level chord recovery.
+
+Rehearsal buyers trust BandScope only when a real take produces the harmony
+they will play. Single-chord unit tests are not enough: a verse-then-chorus
+song must keep distinct section answers. This module synthesizes a two-section
+rehearsal take (C major, then G major) and measures duration-weighted chord
+symbol recall against those true labels (Harte et al., 2005; McVicar et al.,
+2014).
+"""
+
+from __future__ import annotations
+
+import numpy as np
+
+from bandscope_analysis.chords.chord_recognizer import ChordRecognizer, TrackedChord
+from bandscope_analysis.chords.section_harmony import summarize_section_harmony
+
+_SAMPLE_RATE = 22050
+_SECTION_SECONDS = 4.0
+_MINIMUM_WEIGHTED_RECALL = 0.70
+_C4_HZ = 261.63
+_G4_HZ = 392.00
+
+
+def _canonical_major_symbol(label: str) -> str:
+    """Keep major-triad spellings; do not treat minor as a match.
+
+    Args:
+        label: Raw recognizer or annotation chord string.
+
+    Returns:
+        ``C`` or ``G`` for those major labels (including a ``:maj`` suffix),
+        otherwise the stripped original so ``Cm`` cannot satisfy a C window.
+    """
+    stripped = label.strip()
+    if stripped.endswith(":maj"):
+        return stripped[: -len(":maj")]
+    return stripped
+
+
+def _major_triad_take(root_hz: float, duration_seconds: float, sample_rate: int) -> np.ndarray:
+    """Render a dry major triad as equal-amplitude sines.
+
+    Args:
+        root_hz: Root frequency in hertz.
+        duration_seconds: Take length in seconds.
+        sample_rate: Samples per second.
+
+    Returns:
+        Mono float32 audio in ``[-1, 1]``.
+    """
+    sample_count = int(sample_rate * duration_seconds)
+    time_axis = np.linspace(0.0, duration_seconds, sample_count, endpoint=False)
+    major_third_hz = root_hz * (2.0 ** (4.0 / 12.0))
+    perfect_fifth_hz = root_hz * (2.0 ** (7.0 / 12.0))
+    waveform = (
+        np.sin(2.0 * np.pi * root_hz * time_axis)
+        + np.sin(2.0 * np.pi * major_third_hz * time_axis)
+        + np.sin(2.0 * np.pi * perfect_fifth_hz * time_axis)
+    ) / 3.0
+    return waveform.astype(np.float32)
+
+
+def _duration_weighted_symbol_recall(
+    segments: list[TrackedChord],
+    truth_windows: list[tuple[float, float, str]],
+) -> float:
+    """Score recovered segments against true section windows.
+
+    The denominator is annotated duration so a silent or smeared boundary
+    cannot inflate the score (McVicar et al., 2014). Matching recognizer
+    intervals are unioned inside each annotation window so duplicate or
+    overlapping estimates cannot count the same section time twice.
+
+    Args:
+        segments: Time-stamped recognizer output.
+        truth_windows: ``(start, end, canonical_chord)`` annotations.
+
+    Returns:
+        Overlap-weighted recall in ``[0, 1]``, or ``0.0`` when the annotation
+        duration is not positive.
+    """
+    annotated_duration = sum(end - start for start, end, _chord in truth_windows)
+    if annotated_duration <= 0.0:
+        return 0.0
+
+    matched_duration = 0.0
+    for truth_start, truth_end, truth_chord in truth_windows:
+        matching_intervals: list[tuple[float, float]] = []
+        for segment in segments:
+            if _canonical_major_symbol(segment["chord"]) != truth_chord:
+                continue
+            overlap_start = max(segment["start_time"], truth_start)
+            overlap_end = min(segment["end_time"], truth_end)
+            if overlap_end > overlap_start:
+                matching_intervals.append((overlap_start, overlap_end))
+
+        matching_intervals.sort(key=lambda interval: (interval[0], interval[1]))
+        if not matching_intervals:
+            continue
+        current_start, current_end = matching_intervals[0]
+        for interval_start, interval_end in matching_intervals[1:]:
+            if interval_start <= current_end:
+                current_end = max(current_end, interval_end)
+                continue
+            matched_duration += current_end - current_start
+            current_start, current_end = interval_start, interval_end
+        matched_duration += current_end - current_start
+
+    return matched_duration / annotated_duration
+
+
+def test_duration_weighted_symbol_recall_unions_duplicate_time() -> None:
+    """Overlapping matching estimates must not count section time twice."""
+    segments: list[TrackedChord] = [
+        {"start_time": 0.0, "end_time": 3.0, "chord": "C", "confidence": "high"},
+        {"start_time": 1.0, "end_time": 4.0, "chord": "C:maj", "confidence": "high"},
+        {"start_time": 4.0, "end_time": 8.0, "chord": "G", "confidence": "high"},
+    ]
+    truth_windows = [(0.0, 4.0, "C"), (4.0, 8.0, "G")]
+
+    assert _duration_weighted_symbol_recall(segments, truth_windows) == 1.0
+
+
+def test_canonical_major_symbol_rejects_minor_as_major() -> None:
+    """Keep ``Cm`` distinct from ``C`` so a minor estimate cannot pass."""
+    assert _canonical_major_symbol("C") == "C"
+    assert _canonical_major_symbol("C:maj") == "C"
+    assert _canonical_major_symbol("Cm") == "Cm"
+    assert _canonical_major_symbol("G:maj") == "G"
+    assert _canonical_major_symbol("Gm") == "Gm"
+
+
+def test_section_harmony_recovers_verse_c_then_chorus_g() -> None:
+    """Recover C then G as section main chords from a two-section take.
+
+    The take is a rehearsal-shaped verse/chorus pair: four seconds of C major
+    followed by four seconds of G major. The recognizer runs on the mixed
+    audio. Section summaries must keep those answers apart, and
+    duration-weighted recall against the true windows must stay at or above
+    70 percent so a boundary smear cannot hide a wrong-song result.
+    """
+    verse = _major_triad_take(_C4_HZ, _SECTION_SECONDS, _SAMPLE_RATE)
+    chorus = _major_triad_take(_G4_HZ, _SECTION_SECONDS, _SAMPLE_RATE)
+    take = np.concatenate([verse, chorus])
+    boundaries = [(0.0, _SECTION_SECONDS), (_SECTION_SECONDS, _SECTION_SECONDS * 2.0)]
+    truth_windows = [
+        (0.0, _SECTION_SECONDS, "C"),
+        (_SECTION_SECONDS, _SECTION_SECONDS * 2.0, "G"),
+    ]
+
+    segments = ChordRecognizer().recognize(take, sr=_SAMPLE_RATE)
+    summaries = summarize_section_harmony(segments, boundaries)
+
+    assert len(summaries) == 2
+    assert _canonical_major_symbol(summaries[0]["main_chord"]) == "C"
+    assert _canonical_major_symbol(summaries[1]["main_chord"]) == "G"
+    assert _duration_weighted_symbol_recall(segments, truth_windows) >= _MINIMUM_WEIGHTED_RECALL
+
+
+def test_section_harmony_keeps_later_c_off_the_opening_window() -> None:
+    """Refuse a song-wide C answer when G occupies the first section.
+
+    The same two triads in reverse order must not collapse to one global
+    chord. Players need the opening chorus to stay G even though C arrives
+    later. This is the temporal complement of the verse-then-chorus lock.
+    """
+    chorus = _major_triad_take(_G4_HZ, _SECTION_SECONDS, _SAMPLE_RATE)
+    verse = _major_triad_take(_C4_HZ, _SECTION_SECONDS, _SAMPLE_RATE)
+    take = np.concatenate([chorus, verse])
+    boundaries = [(0.0, _SECTION_SECONDS), (_SECTION_SECONDS, _SECTION_SECONDS * 2.0)]
+    truth_windows = [
+        (0.0, _SECTION_SECONDS, "G"),
+        (_SECTION_SECONDS, _SECTION_SECONDS * 2.0, "C"),
+    ]
+
+    segments = ChordRecognizer().recognize(take, sr=_SAMPLE_RATE)
+    summaries = summarize_section_harmony(segments, boundaries)
+
+    assert len(summaries) == 2
+    assert _canonical_major_symbol(summaries[0]["main_chord"]) == "G"
+    assert _canonical_major_symbol(summaries[1]["main_chord"]) == "C"
+    assert _duration_weighted_symbol_recall(segments, truth_windows) >= _MINIMUM_WEIGHTED_RECALL