transform op• 데이터 종류: table → signal
• 호출: import fullseye as fs; fs.ledger.istft(transform)(구현을 직접 호출하려면 import acoustics; acoustics.istft(transform), 원장에서 가져오려면 opsacoustics.get("istft"))
가중 중첩 가산으로 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. 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 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.