transform op• 데이터 종류: table → signal
• 호출: import acoustics; acoustics.istft(transform)(또는 opsacoustics.get("istft"))
가중 중첩 가산으로 :func:stft 를 엄밀히 역변환합니다.
> 아래 상세 설명은 원문입니다 —— 요약과 제목은 번역되어 있습니다.
Weighted overlap-add divides the synthesised sum by the overlap sum of the
*squared* window, which makes the reconstruction exact for any window and
hop satisfying NOLA, not only for the COLA pairs. :func:stft refuses the
NOLA violation up front, so if the transform was produced by it the inverse
cannot be lossy.
Measured round-trip error, `max |x - istft(stft(x))|` on 4096 samples of
white noise (float64, so 2.2e-16 is one ulp of the largest sample):
=============== ==== ==== ========= =========
window win hop max error nola_min
=============== ==== ==== ========= =========
hann 256 128 1.33e-15 0.5
hann 256 64 1.33e-15 1.5
hann 256 255 2.73e-12 2.27e-08
hamming 256 128 1.33e-15 0.5832
blackman 512 128 1.33e-15 1.206
flattop 256 64 1.33e-15 0.396
boxcar 256 128 8.88e-16 2.0
hann (nfft 512) 256 128 1.33e-15 0.5
=============== ==== ==== ========= =========
Read the third row's two columns together. `hop = 255` on a 256-sample
window overlaps by one sample, which breaks plain (unweighted) overlap-add
completely; weighted overlap-add still inverts it, but only to 2.7e-12
rather than 1.3e-15, because the squared-window overlap sum falls to
2.3e-08 and the reconstruction divides by it. NOLA is satisfied and the
result is four orders of magnitude less accurate than every other row —
which is why `nola_min` is *returned* and not merely checked. A NOLA
minimum that is small but positive is a conditioning warning, and there is
no threshold at which it stops being one, so no threshold is invented here.
Raises `ValueError: a dict missing any key :func:stft` writes, a
`spectra whose shape disagrees with the recorded nfft` / frame count,
or a non-complex `spectra`.
• acoustic_condition_monitoring 패밀리 가이드
• 샘플 데이터 카탈로그(DL URL / 라이선스) —— 2-D 는 skimage.data(BSD/public)+ 합성, 3-D 는 실데이터 소스(Stanford/PDS 등)의 DL URL.
• 연산자의 내력·참고문헌 —— 이 연산자 족의 바탕이 된 연구/기법의 출처.
• 알고리즘의 정전(저자·연도)과 용도는 위의 패밀리 사용 가이드에 적혀 있습니다.
• acoustic_condition_monitoring — py -3.11 examples/acoustic_condition_monitoring.py
signal 를 입력으로 받는 것)stft · envelope_spectrum · spectral_kurtosis · cepstrum · angular_resample · order_spectrum · octave_spectrum · weighting_response
transform)*Provenance: acoustics.py — ACOUSTICS 연산자 레지스트리. 이 op 노트는 tools/opdocs.py md 가 자동 생성합니다(직접 편집하지 마세요).*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.