spectrogram — ONED `signal` op

• Data kinds: signal → image2d

• Call: import fullseye as fs; fs.ledger.spectrogram(x, rate=1.0, win=256, hop=None) (to call the implementation directly, import dsp; dsp.spectrogram(x, rate=1.0, win=256, hop=None); from the registry, ops1d.get("spectrogram"))

• Return value through the ledger: fullseye.ledger.spectrogram(...) returns **only the declared out type image2d** (the underlying function also returns auxiliary values). When you need what was dropped, use fullseye.ledger.spectrogram.raw(...) or call dsp.spectrogram directly.

• Underlying return: (freqs, times, S) — S が本体

Usage

STFT magnitude spectrogram -> `(freqs, times, S) with S shape (n_freqs, n_frames). Hann-windowed; *hop* defaults to win//2`.

**Same raw convention as :func:spectrum, but a different divisor.** Each

column is the unnormalised `|rfft(frame * hann(win))|`, so it is not an

amplitude either — and dividing by `2/win` is *wrong* here, because the

Hann window has already thrown away part of the signal. The correct one-sided

amplitude conversion divides by the window's coherent gain::

w = np.hanning(win)

amp = S * (2.0 / w.sum()) # bins 1 .. win/2-1; DC / Nyquist: 1/w.sum()

Measured on a unit sine at a bin centre (`rate = 16000` Hz, 1000 Hz,

amplitude exactly 1.0, `win = 256`): the raw column peak is

`63.7497786196906; * 2/win gives 0.49804514546633283` (too small by

exactly the Hann coherent gain `sum(w)/win = 0.498046875`), while

`* 2/sum(w) gives 0.9999965273676957`. Only the second one is the

amplitude that was actually in the signal.

Peak *positions*, frame-to-frame ratios and any dB *difference* are unaffected

by either factor. This function returns magnitudes only — the phase is

discarded, so it cannot be inverted; use `acoustics.stft / acoustics.istft`

for a round-trip.

References (sample data, literature)

• Sample-data catalog (download URLs / licences) — 2-D uses skimage.data (BSD/public domain) plus synthetic images; 3-D lists download URLs for real data sources (Stanford, PDS, …).

• Operator provenance and references — the sources of the research/methods this op family came from.

• The canonical algorithm (author, year) and its uses are named in the family usage guide above.

Runnable examples (verified samples that actually call this op)

• acoustic_condition_monitoring — py -3.11 examples/acoustic_condition_monitoring.py

Ops the type connects to (they accept `image2d` as input)

—

Same category (`signal`)

lowpass · highpass · bandpass · envelope · rms · local_std · quantize · companding_mu_law

*Provenance: dsp.py — ONED operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*

spectrogram — ONED signal op