transform op• Data kinds: signal → table
• Call: import fullseye as fs; fs.ledger.stft(x, rate, win=256, hop=None, window='hann', nfft=None, scaling='none') (implementation को सीधे call करना हो तो import acoustics; acoustics.stft(x, rate, win=256, hop=None, window='hann', nfft=None, scaling='none'), registry से लेना हो तो opsacoustics.get("stft"))
short-time Fourier transform जो phase को बनाए रखता है और जिसे invert किया जा सकता है।
> नीचे का विस्तृत विवरण मूल text में है — सारांश और headings का अनुवाद हो चुका है।
dsp.spectrogram returns magnitudes, which is all a display needs and strictly less than an analysis needs: a magnitude spectrogram cannot be turned back into a signal, so there is no path in dsp that filters or modifies a signal in the time-frequency plane and comes back. This is that path, and the test of it is that the round trip is exact.
Returns a dict (the transform plus everything istft needs to undo
it):
spectra
complex128 (n_freqs, n_frames), same orientation as
dsp.spectrogram.
freqs, times
bin centre frequencies in Hz and frame start times in seconds. Frame
time 0.0 is the first *original* sample, so the leading pad does not
shift the time axis.
times[0] is therefore negative, and that is the consequence of
the sentence above rather than an error: the first frames start inside
the leading pad. Measured on 1.0 s at 16 kHz with win=256, hop=128,
times[0] = -0.016000 and times[-1] = 1.000000, a span 1.6 %
wider than the record. Do not hand times[0] and times[-1]
straight to a plot's time extent — the picture then claims a
time-frequency surface wider than the recording, whose outermost columns
are half-empty frames. Use times[interior], whose first value is
exactly 0.000000, for anything that has to line up with the signal.
rate, win, hop, nfft, length, pad_left, scale, scaling, window, window_values
the geometry, kept so the inverse needs no arguments.
nola_min
the smallest value of the squared-window overlap sum over the original
samples. Reconstruction divides by this sum, so a value of zero means
some sample is not reconstructible; it is refused up front rather than
producing a hole.
interior
boolean mask over frames, true for the frames that lie entirely inside the original signal. The transform pads by a full window at each end so
that inversion is exact, and the frames straddling that pad see part
zeros — they are correct as coefficients but they are not representative,
and any statistic averaged over *all* frames is therefore biased low.
Measured on 16384 samples of unit-variance white noise, win = 1024,
hop = 512: the "density" spectrum integrates to 0.9073 over all 35
frames and to 0.9933 over the 31 interior ones (the signal's own variance
is 0.9923). spectral_kurtosis uses this mask for exactly that
reason — a half-empty frame looks impulsive.
Normalisation is explicit, because a windowed spectrum has no single natural amplitude and a plausible-looking dB number is the usual result of leaving it implicit. scaling selects a real factor applied to every coefficient, recorded as scale and divided out again by istft:
• "none" (default) — the raw rfft of the windowed frame.
• "amplitude" — 2 / sum(w). A sinusoid of amplitude A sitting on a bin centre then reads |Z| = A. Measured on a 1 kHz, amplitude-0.7 tone at 16 kHz with a 256-sample periodic Hann, over the interior frames:
|Z| ranges 0.699999999999999 to 0.700000000000001.
DC and Nyquist read twice their amplitude under this convention (they are
not two-sided), which is the standard caveat and is not corrected for.
• "density" — sqrt(2 / (rate * sum(w**2))), so |Z|**2 is a single-sided power spectral density in units^2/Hz. Measured on 16384 samples of white noise at 16 kHz (win 1024, hop 512): the PSD integrates to 0.9933 over the interior frames against the record's own variance 0.9923, and to 0.9073 if the pad frames are included — see interior.
Raises ValueError: non-1-D / non-finite / complex / masked input, rate <= 0, a string or bool rate, hop outside [1, win], nfft < win, an unknown window, an all-zero window, a transform over MAX_STFT_ELEMENTS, and a window/hop pair whose squared overlap sum touches zero (NOLA violated — the round trip would be silently lossy).
• acoustic_condition_monitoring family guide
• Sample data catalog (DL URL / license) — 2-D के लिए skimage.data (BSD/public) + synthetic, 3-D के लिए real data sources (Stanford/PDS आदि) के DL URL।
• Operators का स्रोत और संदर्भ — इस op family के मूल research/methods के स्रोत।
• Algorithm का मानक स्रोत (लेखक, वर्ष) और उपयोग ऊपर की family usage guide में दिए गए हैं।
• acoustic_condition_monitoring — py -3.11 examples/acoustic_condition_monitoring.py
table को input के रूप में लेते हैं)transform)*Provenance: acoustics.py — ACOUSTICS operator registry. यह per-op note tools/opdocs.py md से अपने-आप generate होता है (हाथ से edit न करें)।*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.