Section and Structure
Section analysis divides a track into longer musical spans such as intro, verse, chorus, bridge, instrumental, and outro. It is a structural estimate: useful for navigation and visualization, but not a replacement for a producer's arrangement notes.
Use it to navigate a track, set loop points around a chorus, or build a structural visualization — anywhere you need an approximate map of the song's large-scale form.
For newcomers, think of section analysis as building a map of the song. It looks at large spans such as an intro, verse-like area, chorus-like area, or bridge-like area, rather than short events like individual beats or chords.
Reading a self-similarity matrix
The central tool is the self-similarity matrix (SSM). It compares every moment of a feature sequence against every other moment.
You can read it as a "which parts sound alike?" table:
| Pattern in the SSM | Meaning |
|---|---|
| Bright cell | These two times sound similar |
| Block along the diagonal | A span is internally consistent, so it may be one section |
| Stripe away from the diagonal | Two different times sound similar, often a repeated chorus or repeated verse |
- frames that sound alike
- novelty
- boundary
Two signals are especially useful:
- Novelty means the SSM changes suddenly. It helps find boundaries.
- Repetition means similar material appears in separate places. It helps group recurring sections.
Novelty alone tends to split too much. Repetition alone can miss one-off parts. libsonare combines both.
Boundaries first
libsonare detects section boundaries by building frame-level features, computing a self-similarity matrix, and looking for novelty peaks. The default feature mix uses MFCC and chroma, so boundaries can come from timbre changes, harmonic changes, or both.
minSectionSec (default 4 s) is not a hard floor on section length. It sets the minimum spacing between novelty peaks, and a merge pass afterwards drops only spans shorter than half of it — so the effective minimum length of a returned section is 0.5 × minSectionSec, and the leading and trailing spans are bounded by the file edges rather than by two peaks. Very short edits, drops, or pickup bars may be merged into neighboring spans.
For long-form input, the boundary detector mean-pools its feature sequence when the self-similarity matrix would exceed the native integer index cap. Boundary time values remain in the original audio timeline; on very long files, the diagnostic frame field refers to the pooled analysis grid, so UI code should place markers from time, not from frame.
Then labels
After boundaries are found, the implementation classifies each span using several clues:
| Clue | What it helps identify |
|---|---|
| Normalized energy | Whether a span feels like a high-energy or low-energy section |
| Chroma similarity to other spans | Whether the same harmonic material returns elsewhere |
| Vocal-likelihood descriptor | Whether the span is likely to contain a lead vocal or vocal-like material |
Typical outcomes are heuristic:
| Pattern | Likely label |
|---|---|
| Repeated, high-energy, vocal-like span | Chorus |
| Repeated, lower-energy span | Verse |
| Low-energy first or last span | Intro / outro |
| Distinctive interior span with low vocal likelihood | Instrumental |
| Distinctive interior span that still reads as vocal | Bridge |
These labels are intentionally heuristic. They are good for orientation, not for declaring a canonical song form.
Why the result is an estimate
Song structure is partly subjective. Two listeners may disagree about the exact start of a chorus, and different genres use different cues. A techno track and a ballad do not announce sections in the same way.
Treat section output as a strong hint for navigation, looping, and visualization. It works best on music with clear repeated sections, and it is weaker on through-composed material (music that keeps developing instead of repeating sections), ambient, or very gradual material. A boundary being a few seconds off is normal, so automatic results are best used as a starting point for review.
How libsonare computes it
BoundaryDetector combines MFCC and chroma features, L2-normalizes them, mean-pools long inputs when needed, builds a cosine self-similarity matrix, computes a checkerboard novelty curve, and picks boundary peaks. SectionAnalyzer turns boundaries into spans, computes RMS energy, chroma descriptors, spectral flatness, and vocal-band energy, then assigns Intro, Verse, Chorus, Bridge, Instrumental, or Outro labels with confidence. The SectionType enum also carries PreChorus and Unknown, but no classifier branch produces them — treat a switch arm for either as dead code, and do not expect a pre-chorus to be detected.
Related: Mel, MFCC, and Timbre, Chroma Features, MIR Overview