level op• 데이터 종류: signal → table
• 호출: import fullseye as fs; fs.ledger.percentile_level(x, rate, percentiles=(10.0, 50.0, 90.0), weighting='A', ref=1.0, window_s=0.125, floor_db=-200.0)(구현을 직접 호출하려면 import acoustics; acoustics.percentile_level(x, rate, percentiles=(10.0, 50.0, 90.0), weighting='A', ref=1.0, window_s=0.125, floor_db=-200.0), 원장에서 가져오려면 opsacoustics.get("percentile_level"))
통계 레벨: L_N 은 시간의 N % 에서 초과되는 레벨.
> 아래 상세 설명은 원문입니다 —— 요약과 제목은 번역되어 있습니다.
The record is cut into non-overlapping blocks of window_s seconds, each block's equivalent level is computed, and L_N is the (100-N)-th percentile of those levels. Non-overlapping rectangular blocks are used rather than an exponential time weighting because the block length is then exactly what the caller asked for and the statistic is exactly a percentile of the returned levels array — an exponential average would make the effective averaging time a function of the signal.
Returns a dict with one key per requested percentile ("L10", "L50", "L90", formatted with %g), plus levels (the per-block levels), times (block start times, s), n_blocks, block_samples, leq (the energy-equivalent level of the whole record), ref, weighting.
Note that L50 is the *median* level and leq is the *energy* level; they are different numbers whenever the signal is not stationary, and the gap between them is itself the usual measure of how fluctuating a record is.
Measured on a two-level test signal (1 s at 16 kHz, first half a 1 kHz sine of amplitude 1.0, second half the same at 0.1, Z-weighted, 0.125 s blocks, 8 blocks): L10 = -3.010300 and L90 = -23.010300 dB — exactly the two constituent levels, 20.000000 dB apart, as they must be for a 50/50 split. L50 = -13.010300 is the interpolated midpoint of the two clusters and leq = -5.977386, which is 17 dB above L90: the energy level sits near the loud half while the median sits between them. On a constant-amplitude signal all three percentiles and leq agree to 3.6e-15 dB.
Raises ValueError: everything _as_signal refuses, a percentile outside [0, 100], a non-positive window_s, a window_s longer than the record (which would give one block and make every percentile the same number while still looking like a statistic), ref <= 0.
• 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
table 를 입력으로 받는 것)level)octave_bands · octave_spectrum · weighting_response · apply_weighting · equivalent_level
*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.