git:20260926.aafcc14 to git:20260926.90148ff

---

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.

**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 file** — mp3/wav of the song (full mix).

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

Bridge…) come from this, not from the analyzer.

## Tool per step

| Phase | Tool | Output |

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

| 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` |

| 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`** | `chords_lv.json`, `chord_proposal.json` |

| 8 Chart | Python → self-contained **HTML** | chart |

`<venv>` = the analysis venv; Whisper and lv-chordia 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.

## 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.

**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

```bash

```

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). Reading the sweep,

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

`references/meter-detection.md`.

**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. **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: find the entry bar and ignore everything before it

(stem bleed).

## 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.

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. 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.

## Phase 8 — Chart

Read `references/chart-and-lyrics.md` before building the chart. Core invariants:

- One row per section; parallel sections get identical row splits.

- Chart bar = audio bar. A virtual bar only where the audio has none (e.g. a rubato hold

the beat tracker skipped) — and mark it.

- Chord dict keyed by `(bar, cell)`.

- **Lyric phrases anchor at the chord they resolve INTO** (Rule 1 — the big one).

- **Show the evidence:** a header line saying where meter, grouping and mode came from

(`provenance`, `mode.json`), and each chord cell styled by its `status` — a "?" on

root disagreements and near-ties, so the player's check goes where it's needed.

- Output: single self-contained HTML, print-friendly, harmonic-notes block at the bottom.

## 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

in seconds what a sweep can only rank.

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; musical placement is not strict timing (Rule 1).

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. Each chart correction touches ~3 places (chord dict, row layout, cascading lyrics).

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.