transform op• 資料種類:無 → table(僅由參數決定的運算子 —— 不接受影像或資料輸入)
• 呼叫: import fullseye as fs; fs.ledger.stft_cola_check(window='hann', win=256, hop=None)(要直接呼叫實作,import acoustics; acoustics.stft_cola_check(window='hann', win=256, hop=None);從台帳取用則 opsacoustics.get("stft_cola_check"))
這組(窗, 跳距)是否滿足 COLA,以及滿足得有多精確。
> 以下的詳細說明為原文 —— 摘要與標題已翻譯。
COLA — the analysis windows summing to a constant over the hop lattice — is what lets plain overlap-add work without a division. It is *not* required by istft, which is weighted, but it is required by anything that overlap-adds modified frames without renormalising, and getting it wrong produces a periodic amplitude ripple at rate/hop Hz that looks like tremolo rather than like a bug.
Returns a dict: cola (bool), constant (the mean of the overlap sum), max_deviation (absolute), relative_deviation, nola (bool), min_squared_sum, plus the geometry.
Measured (periodic windows, relative_deviation of the plain sum):
======== ==== ==== ================== ======== ====
window win hop relative_deviation constant COLA
======== ==== ==== ================== ======== ====
hann 256 128 4.44e-16 1.00 yes
hann 256 64 2.22e-16 2.00 yes
hann 256 85 1.48e-03 1.506 no
hamming 256 128 2.06e-16 1.08 yes
blackman 256 128 1.91e-01 0.84 no
blackman 256 64 3.97e-16 1.68 yes
boxcar 256 128 0.00e+00 2.00 yes
======== ==== ==== ================== ======== ====
The two blackman rows are the useful ones: the same window is COLA at hop = win/4 and 19 % off at hop = win/2, so "which window" is not the question — the pair is. The boxcar row was worth measuring rather than
assuming: a rectangular window at 50 % overlap sums to exactly 2 and is COLA, which is the opposite of the usual intuition about it. Note also that the constant is not 1 in general — an overlap-add that does not divide by it is off by a *gain*, which is the failure that looks like a working system.
Raises ValueError: unknown / all-zero window, hop outside [1, win], win outside [2, MAX_WINDOW].
• acoustic_condition_monitoring 族使用指南
• 範例資料目錄(下載 URL / 授權) —— 2-D 用 skimage.data(BSD/公有領域)加合成圖,3-D 給出真實資料源(Stanford/PDS 等)的下載 URL。
• 運算子來歷與參考文獻 —— 該運算子族所依據的研究/方法出處。
• 演算法的正典(作者、年份)與用途見上面的族使用指南。
• acoustic_condition_monitoring — py -3.11 examples/acoustic_condition_monitoring.py
table 作為輸入)transform)*Provenance: acoustics.py — ACOUSTICS 運算子登記表。本條目由 tools/opdocs.py md 自動產生(請勿手動編輯)。*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.