git:20261001.e6d2ad2 to git:20261001.b6c3a1b

---

name: song-analysis

description: Use when a song recording has to become tempo, time signature, key, stems, a bass line, chords per bar, lyric timing, a drum-pattern read, or a chord+lyric chart for a band — including songs in odd or unknown meters.

---

# Song Analysis — recording → bars, chords, lyrics, chart

Song-agnostic pipeline, validated over many real projects (dream pop, Greek laiko, punk

reinterpretations, drone/devotional). Works for any song, genre, key or meter.

**Deliverable:** one self-contained HTML per song — the chord+lyric chart plus the

commentary (Phase 8). The JSON artifacts are working files, not the answer.

**References (read on demand):**

- `references/meter-detection.md` — how the meter sweep works, reading its output, calibration

- `references/stems.md` — separation options, speeds, cache, quality checks

- `references/chord-proposal.md` — chord benchmark, the triad cross-check, the major-bias trap

- `references/chart-and-lyrics.md` — chart structure, the lyric-placement rules, HTML output

- `references/drum-pattern-analysis.md` — kick/snare pattern reading; optional ADTOF

- `references/modal-theory.md` — the 7 modes and the mode test, power chords, Byzantine ≠ Phrygian

- `references/lead-and-keys-extraction.md` — isolated guitar/piano lines via `htdemucs_6s`

- `references/environment-setup.md` — venvs, pinned versions, folder layout, this machine

## Inputs

1. **Audio** — mp3/wav of the full mix. **Only a name?** Phase 0 fetches the recording

(opt-in; the user confirms the pick before anything is downloaded).

2. **Lyrics** — canonical text from an official source. Section labels (Verse, Chorus,

Bridge…) come from this, not from the analyzer. **Not supplied? Get them before

Phase 1** — Phase 6 needs them and a late search stalls the run.

`scripts/lyrics_from_page.py <URL> --out lyrics.txt --chords-out analysis/tab_chords.json`

moves a lyrics or chord-tab page's text into the song folder and prints only counts.

**Never retype lyrics through your own output** — copyrighted text is blocked

("Output blocked by content filtering policy") and search tools won't reproduce it

either. If a site answers 403/404, search for another (songlyrics.com,

lyricsondemand.com, the band's Bandcamp, a chord-tab site). Such text is unofficial:

say so in the provenance. Whisper only times it (Phase 6); use Whisper's text as the

lyrics only when no page has the song.

## Tool per step

| Phase | Tool | Output |

|---|---|---|

| 0 Audio (optional) | **`scripts/fetch_audio.py`** (yt-dlp: YouTube Music first; the user confirms the pick) | `<Artist> - <Title>.wav`, `source.json` |

| 1 Pulse + key | **`scripts/foundation.py pulse`** (madmom beats + CNN key) | `foundation.json` (step 1) |

| 2 Stems | **demucs `htdemucs_ft -d mps`** | `stems/htdemucs_ft/<song>/` |

| 3 Meter | **`scripts/detect_meter.py`** → **`scripts/foundation.py meter`** | `meter.json`, `foundation.json` (step 2) |

| 4 Bass, tonic, mode | **`scripts/bass_notes.py`** (pyin) → **`scripts/mode_test.py`** | `bass.mid`, `bass_per_cell.json`, `mode.json` |

| 4b Performance | **`scripts/drum_transcribe.py`** (ADTOF) + **`scripts/strum_pattern.py`** | `drum_hits.json`, `drums.mid`, `strum.json` |

| 5 Lyrics | **`scripts/whisper_gated.py`** (gated by the vocal stem; stem and mix) | `lyrics.json`, `lyrics_mix.json` |

| 6 Sections | **`scripts/align_lyrics.py`** (canonical lyrics ↔ word times) | `lyrics_aligned.json`, `sections.json` |

| 7 Chords | **`scripts/lv_chords.py`**, cross-checked by **`scripts/chord_proposal.py`**, then **`scripts/tab_compare.py`** | `chords_lv.json`, `chord_proposal.json` |

| 8 Chart | **`scripts/stem_activity.py`** → **`scripts/chart_html.py`** (per-song data file) | `stem_activity.json`, `band_level.json`, self-contained **HTML** chart |

`<venv>` = the analysis venv; Whisper, lv-chordia and yt-dlp have their own

(`references/environment-setup.md`). Below, `ST=stems/htdemucs_ft/<song>`. After any

phase, `scripts/validate_artifacts.py analysis` checks the hand-offs (grid, grouping,

cell coverage, time order) — run it before building on a result.

**Run order.** Start stems (Phase 2) in the background first — several songs one after

another, not at once, on one GPU — and do Phase 1 and the lyrics meanwhile. Whisper

(Phase 5) needs only the vocal stem: background it while the meter is settled. Everything

keyed by `(bar, cell)` — lyric alignment, chords, mode — waits for the **downbeat check**

in Phase 4; moving beat 1 afterwards means re-running all of it.

## Phase 0 — Audio from a song's name (optional)

Only when the user names a song instead of giving a file; needs the yt-dlp venv

(`references/environment-setup.md`, step 6).

```bash

<ytdlp-venv>/bin/python scripts/fetch_audio.py --artist="<Artist>" --title="<Title>" \

--out-dir="<song folder>" [--duration=<length of the user's copy, s or m:ss>]

```

YouTube Music first (the labels' own uploads), then YouTube; uploads titled live, cover,

karaoke, remix, sped up and the like are rejected, the rest ranked — 5–10 s, nothing

downloaded. Spell the names properly (they name the WAV); Greek matches in either

alphabet. `--duration` settles it: the label's or the artist's upload within ~2 s of the

user's copy comes first.

1. Tell the user the `* top pick` line — title, channel, length, album and year — and wait

for a yes. Label uploads sharing a title can be different recordings (a live album, a

re-recording, a budget compilation): name the album. Not label audio, or "misses a word

of the song"? Show the top three and let the user choose.

2. On a yes, run the printed command; for another candidate, change its `--url=` (and

`--artist=` if the performer differs). It writes `<Artist> - <Title>.wav` and

`source.json` (URL, channel, format, dates, licence) into the song folder; cite it in

the chart's provenance line (`references/chart-and-lyrics.md`).

3. Fetch only what the user has the right to study: YouTube's Terms restrict downloading.

No cookies or logins, ever.

## Phase 1 — Pulse and key (no meter assumed)

```bash

<venv>/bin/python scripts/foundation.py pulse <song.mp3> --out analysis/foundation.json

```

Tracks beats with **no bar assumption** — never pass `beats_per_bar=(3, 4)`: a constrained

candidate list can't say "neither", so it returns the least-bad option with full

confidence and every bar count inherits it. Writes `pulse_bpm`, `beat_times`,

`key_top2` and `tempo_octave`: a pulse above 160 BPM is flagged as probably eighth notes

(a 91 BPM soul track was tracked at 182), one below 70 as possibly half-time. If the

user's count disagrees, re-run with `--min-bpm`/`--max-bpm` to force the other octave.

A GRID REPAIR over a long stretch (a quiet intro) is the same slip — check the octave.

**Cross-check online:** look the song up on songbpm.com and tunebat.com (page tools work

there). They catch a gross error, but often list the doubled octave (a song at 87 shown as

174, "87 half-time") and the melody's key — they don't decide the octave or the tonic;

note what they say in the provenance.

**Key: the top two are the melody's key, not the tonic.** The madmom CNN beats chroma +

Krumhansl by ~20 MIREX points and fixes relative-major/minor flips, but knows only 24

major/minor keys, and its probability is not a confidence on modal or drone material (a

Lydian track came out as the wrong key at p = 0.59). After Phase 4, take the pitch class

the bass sustains longest: if it disagrees, trust the pedal as the tonic and name the mode

with the mode test (`references/modal-theory.md`).

## Phase 2 — Stems

**Decide the split from the purpose, early** (ask with the inputs if unclear: chart only,

drums/bass, or the guitar/piano parts too — e.g. for a Live sketch or remix):

| Purpose / song | Run | Keep |

|---|---|---|

| **default** (a full analysis, a Live sketch or remix) | `htdemucs_ft`, **then** `htdemucs_6s` | the four from `_ft` + only `guitar.wav` / `piano.wav` from `_6s` |

| only a chart, the drums or the bass, and the user says so | `htdemucs_ft` (4 stems) | all four |

The 4-stem `_ft` is the cleaner split (vocals, drums, bass): never replace it with the

6-stem run, which bleeds keys into the bass. In `other.wav` the guitars, keys and pads are

one mix, so a guitar-led song needs the extra run.

```bash

ffmpeg -v error -i <song.mp3> -c:a pcm_s16le /tmp/<song>.wav # decode first (see below)

demucs -n htdemucs_ft -d mps -o stems /tmp/<song>.wav # → $ST/{bass,drums,vocals,other}.wav

demucs -n htdemucs_6s -d mps -o stems /tmp/<song>.wav # guitar/piano songs: keep only

# stems/htdemucs_6s/<song>/{guitar,piano}.wav

```

**Decode MP3 to WAV first:** demucs 4.1 reads MP3 without the gapless trim, so every stem

starts 25 ms late against the mix — enough to smear bass and beat timing. `-d mps` on Apple

Silicon (otherwise CPU, ~3× slower). A fetched WAV needs no decode: copy it to

`/tmp/<song>.wav` (a name without spaces, so `$ST` needs no quoting). **Check the model cache first** —

uncached, `htdemucs_ft` downloads 4 × 84 MB silently. Listen to `bass.wav` alone: piano or

guitar in it means separation struggled. Faster/other options and timings:

`references/stems.md`.

## Phase 3 — Meter (sweep every cycle, then decide)

```bash

<venv>/bin/python scripts/detect_meter.py <song.mp3> --drums $ST/drums.wav \

--bass $ST/bass.wav --other $ST/other.wav \

--foundation analysis/foundation.json --json analysis/meter.json

<venv>/bin/python scripts/foundation.py meter [--pulse-unit 8] [--use-alternative | --cycle N ...]

```

The sweep folds band-limited onsets onto every period 2–25 and ranks by accent contrast;

songs without a kit still carry the cycle in the bass and harmony, so always pass

`--bass` and `--other`. It reports a cycle, the downbeat (counted with the kick) and the

main split (`grouping`, e.g. 6+5) — or **INCONCLUSIVE**.

If the sweep is INCONCLUSIVE or the pulse is slow, it also tries a 2× (eighth-note) grid

— a half-time grid hides odd cycles — and prints the result with the command to adopt it.

It warns when successive beat intervals run in a 3:2 ratio: the tracker may be following

aksak groups (2+2+3) as uneven beats.

`foundation.py meter` then writes `beats_per_bar`, `pulse_unit`, `time_signature`,

`grouping`, `downbeat_times`, `num_bars`, `bar_bpm`, `tempo_drift_pct` (spread of the middle

90% of bars), `tempo_outlier_bars` (more than 5% off) and `live_tempo`. A tracker that drops

a tempo octave for a stretch is repaired in Phase 1 (`grid_repair` lists where).

**It stops with a question instead of guessing** — put that question to the user and

re-run with their answer: INCONCLUSIVE (count along), a phrase-length cycle (8/16 → 4/4?),

a bare duple (2/4 or 4/4?), two equally strong accents (which one is beat 1?), or an odd

cycle (eighth or quarter pulse — 11/8 vs 11/4 doubles Live's tempo). INCONCLUSIVE with

one band clear (often the bass, when the kit enters late) — put that band's cycle and

beat 1 to the user as the proposal, then `--cycle`/`--downbeat-pulse`. Reading the sweep,

calibration, and why beat trackers' own downbeats aren't used:

`references/meter-detection.md`.

**Four-on-the-floor kits** (kick on every beat) give the kick band no cycle, so the sweep

reports INCONCLUSIVE or a bare duple; the bass and harmonic bands carry the meter, and a

2-and-4 backbeat can't tell pulse 0 from pulse 2. When you must pick beat 1 yourself,

weigh (strongest first): the first full-band hit at the song's start (a drum-only pickup

before it is common); the phase where bass and chords change (Phase 4 downbeat check).

Cymbal accents alone are weak — a crash pattern can sit on beat 3. `--cycle` and

`--downbeat-pulse` are recorded as `user` in `provenance`; if you chose them, not the

player, say so in the chart header and list it as an open question.

**Cells.** Everything downstream (bass, chords, lyrics, chart) is keyed by `(bar, cell)`,

one cell per group in `grouping`: 4/4 → 2+2 (the familiar half-bars), 3/4 → one cell,

6/8 → 3+3, 11/8 as 6+5 → two unequal cells. Never split an odd bar at its midpoint.

**Sanity checks:** `tempo_drift_pct` above ~3% means one BPM misplaces notes by the end —

place them through the beat grid (`ableton-mcp` → "Timing"). Watch half/double time on

slow songs (60–90 BPM) — count along. A `num_bars` far from

`duration ÷ (beats_per_bar × beat)` means rubato, a wrong tempo octave or a wrong meter.

## Phase 4 — Bass, tonic and mode

```bash

<venv>/bin/python scripts/bass_notes.py $ST/bass.wav --foundation analysis/foundation.json \

--bass-entry-bar <N> --outdir analysis

<venv>/bin/python scripts/mode_test.py --other $ST/other.wav \

--foundation analysis/foundation.json --bass-cells analysis/bass_per_cell.json \

[--sections analysis/sections.json] --out analysis/mode.json

```

`bass_notes.py`: **pyin** (why not CREPE: the `bass-transcribe` skill) → `bass.mid` and

each pitch class's sounding time per `(bar, cell)`. basic-pitch only if the part is

polyphonic.

**Downbeat check — before anything else keyed by bar.** `bass_notes.py` counts where in

the bar the bass changes pitch. If a later cell takes most changes (a loop moving on beat

3 of 4/4), it prints `DOWNBEAT CHECK` with the corrected `--downbeat-pulse`: re-run

`foundation.py meter` with it, then `bass_notes.py`. The one legitimate exception is

harmony deliberately pushed off beat 1 — confirm with the song-start hit before shifting.

**The tonic is the pitch class the bass sustains longest** — not the key

estimate. `mode_test.py` then names the mode by dueling characteristic degrees bar by bar

(3 vs ♭3, 4 vs ♯4, 7 vs ♭7, 2 vs ♭2, 6 vs ♭6); a degree that doesn't sound doesn't vote,

so a drone with no 6th reports "6th undetermined" instead of a guess. On a player-verified

Lydian track: tonic E (57% of bass time), ♯4 in 32/32 bars — where the key CNN said G♯

minor. With `sections.json` it runs per section and flags a tonic change only if it holds

8+ bars. Method: `references/modal-theory.md`.

**Spell pitch classes from the key signature.** Flats for flat and neutral keys; sharps

for sharp keys — in F♯ minor a flat table writes the tonic `G♭m` and the dominant `D♭7`.

- Sub-0.5 s detections are noisy; ≥ 0.7 s sustained notes are usually right.

- Bass pedals, walks, sits on 3rds/5ths — it's a *clue* to the root, not the chord.

- **Bass enters late** in most songs: `bass_notes.py` prints `BASS ENTRY: bar N` when it

finds one — re-run it with `--bass-entry-bar N` and pass the same to `chord_proposal.py`

(earlier notes are stem bleed).

## Phase 4b — Performance: the drummer's hits and the guitar's strumming

```bash

<adtof-venv>/bin/python scripts/drum_transcribe.py $ST/drums.wav \

--foundation analysis/foundation.json # → drum_hits.json, drums.mid

<venv>/bin/python scripts/strum_pattern.py stems/htdemucs_6s/<song>/guitar.wav \

--foundation analysis/foundation.json [--sections analysis/sections.json] # → strum.json

```

A chart says which chord; a band (and a Live sketch) also needs **how** it's played.

`drum_transcribe.py` labels every hit (ADTOF: kick, snare, hat, toms, cymbals; the stem

is peak-normalised first) with a velocity from the stem, and lists the **fill bars** — a

tom when toms are rare; twice the usual tom bar when the drummer grooves on the toms (a

surf beat put toms in half the bars); or a crowded last beat. On the first two songs they

sat before section changes. It can't tell crash from ride well (one cymbal class). A

guitar under 4 attacks a bar holds its chords (reverb, surf): no strum pattern is read. `strum_pattern.py` snaps the

guitar's onsets to the 16ths of each bar and prints each section's pattern (`D`/`U` by

the hand-motion convention, not measured) — on its first song it showed the verse push on

4& the player described. Both keep every hit's real time and strength for the sketch.

Skip 4b when the purpose is only a chart.

## Phase 5 — Lyric timing (Whisper, gated by the vocal stem)

```bash

<whisper-venv>/bin/python scripts/whisper_gated.py $ST/vocals.wav [--language en] \

--out analysis/lyrics.json

<whisper-venv>/bin/python scripts/whisper_gated.py $ST/vocals.wav --mix <song.mp3> \

[--language en] --out analysis/lyrics_mix.json # when canonical lyrics exist

```

An RMS gate on the vocal stem, fed to Whisper as `clip_timestamps`, keeps it from

transcribing silence; the script also sets large-v3-turbo's word-alignment heads and drops

words whose *whole* span is silent. **Which audio to transcribe is song-dependent:**

measured with this exact setup on 6 benchmark songs, the vocal stem averaged 19.8% word

error and the full mix 23.6%, but either one won by up to 25 points on single songs

(published results favouring the mix used Whisper without the gate). With canonical

lyrics, transcribe both and let Phase 6 keep the one matching more of the lyrics — that

picked the better transcription every time (17.9%). Without lyrics, use the vocal stem.

Force `--language` when detection wobbles (chant, non-English). Keep repeated lines —

mantras are lyrics — until Phase 6 has aligned them. Whisper invents subtitle phrases over

music ("Thanks for watching"): known ones are dropped, and `CHECK BY EAR` names any short

phrase standing alone in an instrumental stretch.

Map each word to a bar:

```python

def locate(t, downbeats, beats_per_bar): # from foundation.json — never assume 4

for i in range(len(downbeats)-1):

if downbeats[i] <= t < downbeats[i+1]:

return (i+1, (t - downbeats[i]) / (downbeats[i+1] - downbeats[i]) * beats_per_bar)

return None

```

## Phase 6 — Sections (canonical lyrics aligned to the word times)

```bash

python3 scripts/align_lyrics.py --lyrics lyrics.txt --words analysis/lyrics.json \

--words analysis/lyrics_mix.json --foundation analysis/foundation.json

# keeps the better transcription → lyrics_aligned.json, sections.json

```

Keeps the **canonical text** and borrows only Whisper's times: a monotonic alignment with

fuzzy word matching (accents and Greek final sigma normalised) pairs canonical words with

heard words; dropped lines — normal on repeats — get times interpolated between matched

neighbours, and lines under 30% matched are flagged; `SUNG, NOT IN THE TEXT` names Whisper

words sung just before a line that the page left out ("I'm coming back…" written

"Coming back…") — check by ear, add them. In `lyrics.txt` a blank line starts a

section and a `[Chorus]` line labels it; each section gets its first line's time and

`(bar, cell)`. On the benchmark, 83% of line starts landed within 1 s (median error

~0.5 s) and 98% of section starts within 2 s. Automatic segmentation can *suggest* boundaries, never decide them:

`as_seg` (barwise CBM on this song's own bars, `penalty_weight=0`) hit 80% of

lyric-anchored boundaries within ±1 bar on the one reference song with section truth,

27% within ±0.5 s.

## Phase 7 — Chords (lv-chordia, cross-checked)

```bash

<venv>/bin/python scripts/chord_proposal.py --other $ST/other.wav --bass $ST/bass.wav \

--foundation analysis/foundation.json --bass-entry-bar <N> \

--out analysis/chord_proposal.json # triad cross-check

<lv-venv>/bin/python scripts/lv_chords.py <song.mp3> --foundation analysis/foundation.json \

--compare analysis/chord_proposal.json --out analysis/chords_lv.json

```

**lv-chordia is the provisional primary reading** — it names 7ths and inversions, which

the triad method can't, and led it on all three benchmark songs (two scored against

another automatic tool, one against a player's ear; `references/chord-proposal.md`). Three

songs is a small sample: it is weakest on rare qualities, and anything beyond 7ths

(add9, ♯11) is untested — read those by ear. Run it on the **full mix**.

**The triad method is the independent cross-check** (bass-root constraint with slash

relaxation, major bias **0**, flip count, per-cell margin). `--compare` lists cells where

the two disagree on the root — check those by ear with the Trap 1 tests.

**Read first, then check against a tab** (when `lyrics_from_page.py` found one):

`tab_compare.py --tab analysis/tab_chords.json --chords analysis/chords_lv.json

--sections analysis/sections.json --bass analysis/bass_per_cell.json` lists per section

the chords only in the tab (never in the bass → a passing chord over a held bass, or

wrong), only in ours (a tab simplification?), and the same root with the other third.

A tab is a second opinion — often simplified or transposed — never the chart's source;

name it and the agreement in the provenance, and its disagreements in the open questions.

**Where the bass drops out, agreement proves little.** In a breakdown both readers hear

only the upper voices, and chords sharing them pass for each other: both said Am where the

player heard G (a chorus-2 turnaround, bass out) — the parallel chorus-1 bar, with bass,

read G. In bass-less cells, chart what the parallel section plays and list it for the ear.

## Phase 8 — Chart

```bash

<venv>/bin/python scripts/stem_activity.py $ST --foundation analysis/foundation.json \

--out analysis/stem_activity.json # per-bar stem levels for the song map,

# + band_level.json: the band's stops

python3 scripts/chart_html.py gen_v1.py # in the song folder; --out to write elsewhere

```

The chart is rendered: you write the decisions (sections, a chord per `(bar, cell)`, each

lyric line at its anchor cell, the header, provenance and commentary) as a `SONG` dict in

`gen_v<N>.py` (a new N per revision); `chart_html.py` reads everything measured from

`analysis/`, so a re-run that keeps the bar grid needs only a re-render (a moved beat 1

shifts every key). Chords may be `chords_lv.json`'s labels (`A:min`, `G/b7`): the renderer

converts them. Read `references/chart-and-lyrics.md` first. Core invariants:

- Each section starts a new row; parallel sections get identical row splits.

- Chart bar = audio bar; the renderer adds none (a final chord may ring on the bar that

starts at the last downbeat).

- **Every word sits where it is sung** (Rule 1, player-checked): a word held across a

half-bar boundary, sung within a dotted eighth before it, sits after it ("…once more"

on the next bar's chord); stop bars (the band out under the voice) are greyed, with no

"?". `placement: 'anchor'` (pickups pulled into the chord the phrase resolves into) only

if the band asks for it.

- **Show the evidence:** the `provenance` line says where meter, grouping and mode came

from (`foundation.json` → `provenance`, `mode.json`); the renderer marks "?" where a

cell's own reading names another root, so the player's check goes where it's needed.

- The commentary (`notes`): per-section harmonic notes in scale degrees,

arrangement/groove notes (entries, drops, turnarounds), and the open questions for the

player (every call you made that the player hasn't confirmed).

**What next (Live optional).** Only if the Live skills are installed and

`mcp__ableton__health_check` answers, ask once: "The chart is done. Want a **sketch in

Live** (the vocal on Live's grid, the chart's chords, the bass line and a drum part, the

original mix muted for A/B) or a **remix** (keep the vocal; new drums, bass and keys on

these chords in a style you pick), or stop here?" Both are `ableton-song-remix` (sketch =

`--mode sketch`); they need Live 12 and the patched Remote Script

(`ableton-mcp/references/remote-script-patch.md`). Otherwise stop at the chart.

## Benchmark

`bench/run_bench.py` scores the artifacts against local ground truth (never committed):

meter by bar length in seconds (so tempo octave doesn't matter), grouping, chords with

mir_eval, section boundaries, mode, lyric WER and line timing. Set `$MWS_BENCH_ROOT` to

the folder with `manifest.json`; `--baseline` fails on a 2-point drop. Human ceiling for

chords: expert annotators agree on 73% (maj/min) / 54% (full labels) of segments.

## Hard rules — the cardinal traps

### Trap 1 — A bass walk is one chord or two; the upper voicing decides

Bass moving within a bar (`C → A♭`) has two readings, and both are common: one held chord

over a moving bass (`Cm/A♭`, or a drone with a walking bass), or **two chords**

(`Cm → A♭`). The bass alone can't tell them apart, and a bass-constrained matcher follows

the bass, so it reports two chords either way. Look above the bass:

- **Re-attack** — a new voicing struck where the bass moves = two chords.

- **Register-pooled chroma** — pool chroma per cell in the chord instrument's register

(high-pass above ~165 Hz). The pitch-class set changes with the bass = two chords; it

stays put = one chord over a moving bass.

### Trap 2 — "Minor key" ≠ minor chords

Key estimators report the **melody's mode**, not chord quality. Many songs put major

triads under a minor melody — the bittersweet sound. Never propagate the key label to

chord quality; test each chord (major vs minor third, `chord-proposal.md`). Modal songs

often pivot between relative major and minor — label the key as a pair (`E♭ / Cm`).

### Trap 3 — The meter you never tested for looks like no meter at all

An odd cycle folded onto the wrong period gives a **flat** profile, not a low one — 11

folded onto 2, 3, 4 or 6 smears to near-uniform. Uniform contrast everywhere means the

right period hasn't been tested yet, never "no time signature". Assume odd meters are

live (`references/meter-detection.md`).

**Verify with a player.** Automated signals are wrong ~30% of the time on modal-mixture

songs. A musician who knows the song is ground truth — plan a verification pass before

declaring the chart done.

## Lessons learned

1. Trust the user's ear over any analyzer. Ask them to count — a player's count settles

2. Never hand an analyzer a candidate list it can't say "none of these" to — a constrained

`beats_per_bar` and a key-gated major bias both produced confident wrong answers that

survived every downstream check.

3. Chord-recognition tools are starting points, never ground truth.

4. Whisper timing is precise to the consonant; the singer pushes into the beat — place

by timing plus the push (Rule 1), and score any placement change against the player's

corrections before showing a new chart. A lyrics page can drop sung words:

`align_lyrics.py` prints `SUNG, NOT IN THE TEXT`.

5. `basic-pitch` duplicates notes at exact octaves and invents low-register content the

stem doesn't contain. Floor it at the instrument's range (C3 for a piano out of

`other.wav`) before pushing anything to Ableton.

6. A chart correction is a data-file edit (chords, sections, lyric anchors) plus a

re-render; the renderer redoes the rows and the lyric cascade.

7. Mirror parallel sections visually; overlay section-boundary pickups in the same cell.

## Local environment

Venvs, interpreters and cached models on this machine: `references/environment-setup.md`

→ "This machine". Scripts here supersede the older helpers in the tooling root.