Source: https://jmrplens.github.io/phonometry/reference/api/environment/impulse-prominence/ # Impulsive-sound prominence (NT ACOU 112) Noise with prominent impulses (hammering, riveting, pile driving) is more annoying than a steady sound of the same equivalent level. **NT ACOU 112:2002** (a Nordtest method) quantifies how *prominent* an impulse is and turns it into a graduated adjustment `KI` added to the measured `LAeq`. The prominence is read from two properties of each impulse's onset in the A-weighted, time-weighting-F level history: how fast it rises (the **onset rate**) and how far it rises (the **level difference**). ## 1. Predicted prominence (clause 7) For each candidate impulse the **predicted prominence** `P` combines the onset rate (in dB/s) and the level difference (in dB) on a logarithmic scale (Formula 1): $$ P = 3\,\lg(\text{onset rate}) + 2\,\lg(\text{level difference}). $$ The coefficients were fitted to listening tests and `P` is designed to peak around 15 for very sudden, loud impulses. The impulse with the **highest** `P` over a 30-minute period governs. A level rise only *qualifies* as an impulse when its onset rate exceeds 10 dB/s (clause 4.5; clause 8 applies the adjustment "for sounds with onset rates larger than 10 dB/s" only): `impulse_prominence` marks non-qualifying events in its `qualifies` mask, warns about them, and never lets them set the governing prominence or a `KI` (the adjustment is 0 dB when no event qualifies). [Watch the high-resolution video (WebM)](https://raw.githubusercontent.com/jmrplens/phonometry/main/.github/images/anim_onset_detection.webm) ```python from phonometry import environmental # Three candidate impulses: (onset rate dB/s, level difference dB). result = environmental.impulse_prominence([1200.0, 300.0, 60.0], [32.0, 18.0, 11.0]) print(result.per_impulse.round(2)) # [12.25 9.94 7.42] print(round(result.prominence, 2)) # 12.25 (the governing impulse) print(round(result.adjustment, 2)) # 13.05 dB result.plot() # the KI(P) curve of section 2 with these impulses marked ``` A single impulse can be evaluated directly with `predicted_prominence`: ```python from phonometry import environmental # P = 3*lg(1000) + 2*lg(30) = 9 + 2.95 = 11.95. print(round(environmental.predicted_prominence(1000.0, 30.0), 4)) # 11.9542 ``` ## 2. Adjustment to LAeq (clause 8) Below a prominence of 5 the impulse is not audible enough to matter; above it the adjustment grows linearly (Formula 2): $$ K_I = 1.8\,(P - 5)\ \text{dB} \quad (P > 5), \qquad K_I = 0 \quad (P \le 5). $$ ```python from phonometry import environmental print(float(environmental.impulse_adjustment(10.0))) # 9.0 dB print(float(environmental.impulse_adjustment(5.0))) # 0.0 dB (at the threshold) ``` The adjustment is applied to `LAeq,30min` from the single event with the highest prominence. The **rating level** over a longer reference time combines the impulse-adjusted equivalent levels of the sub-intervals (clause 8, Note 1): $$ L_{Ar,T} = 10\,\lg\!\left(\frac{1}{T}\sum_N \Delta t_N\, 10^{(L_{Aeq,N} + K_{I,N})/10}\right). $$ ```python from phonometry import environmental # Two 30-min periods: one impulsive (KI = 7.6 dB), one quiet. print(round(environmental.rating_level([72.0, 66.0], [7.6, 0.0], [30.0, 30.0], 60.0), 2)) # 76.78 dB ``` The `ImpulseProminenceResult` carries the `per_impulse` prominences, the governing `prominence` and its `adjustment`, and its `.plot()` draws the `KI(P)` curve with the impulses marked. The method is a supplement to the environmental-noise measurement of ISO 1996-2.

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```python import numpy as np import matplotlib.pyplot as plt from phonometry import environmental # One line for the adjustment curve with the impulses marked: environmental.impulse_prominence([1200.0, 300.0, 60.0], [32.0, 18.0, 11.0]).plot() plt.show() # By hand, the left panel — P vs onset rate for three level differences: orate = np.logspace(1, 4, 200) for ld in (5.0, 15.0, 30.0): plt.plot(orate, environmental.predicted_prominence(orate, np.full_like(orate, ld)), label=f"LD = {ld:g} dB") plt.xscale("log"); plt.legend(); plt.show() ```

## 3. Objective measurement from a signal (ISO/PAS 1996-3) NT ACOU 112 takes the onset rate and level difference as inputs. **ISO/PAS 1996-3:2022** keeps the same prominence and adjustment formulae but adds the *objective measurement chain* that reads those quantities straight from a calibrated recording. `impulsive_sound_adjustment` A-weights the signal, applies time weighting F (`τ` = 125 ms), samples the level history `LpAF` every 10-25 ms, detects each onset (the contiguous stretch whose gradient exceeds 10 dB/s, merging events less than 50 ms apart), measures its level difference `LD = Le − Ls` and its least-squares onset rate `OR`, and returns the governing adjustment `KI` with the source category of clause 7: *not impulsive* (`KI = 0`), *regular impulsive* (`0 < KI ≤ 5`) or *highly impulsive* (`KI > 5`). The onset detection can be exercised on a level history directly with `detect_onsets`, which is convenient for meters that already log `LpAF`: ```python import numpy as np from phonometry import environmental # An LpAF history sampled every 20 ms: quiet, a 30 dB rise over 0.30 s, steady. dt = 0.02 levels = np.concatenate([ np.full(10, 40.0), 40.0 + 30.0 * np.arange(1, 16) / 15, # a straight 100 dB/s onset to 70 dB np.full(15, 70.0), ]) onset = environmental.detect_onsets(levels, dt)[0] print(round(onset.onset_rate), round(onset.level_difference)) # 100 30 print(round(onset.prominence, 2)) # 8.95 ``` From a calibrated time signal (in pascal) the whole chain runs end to end: ```python result = environmental.impulsive_sound_adjustment(signal, fs) print(result.category) # e.g. 'highly impulsive' print(round(result.adjustment, 1)) # KI in dB (0.0 to about 9 dB in typical cases) print(round(result.adjusted_laeq, 1)) # LAeq + KI result.plot() # the LpAF history with the detected onsets (needs matplotlib) ```

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```python import numpy as np import matplotlib.pyplot as plt from phonometry import environmental # Three hammer strikes over a 55 dB(A) background, 6 s at 48 kHz. fs = 48000 rng = np.random.default_rng(7) t = np.arange(int(6.0 * fs)) / fs background = rng.standard_normal(t.size) background *= 2e-5 * 10 ** (55 / 20) / np.sqrt(np.mean(background ** 2)) signal = background.copy() for onset_time in (1.0, 2.6, 4.2): decay = np.exp(-(t - onset_time) / 0.08) * (t >= onset_time) strike = decay * rng.standard_normal(t.size) window = (t >= onset_time) & (t < onset_time + 0.1) strike *= 2e-5 * 10 ** (95 / 20) / np.sqrt(np.mean(strike[window] ** 2)) signal += strike res = environmental.impulsive_sound_adjustment(signal, fs) print(res.category, round(res.prominence, 2), round(res.adjustment, 2)) # highly impulsive 11.34 11.42 # One line: the level history with the onsets, the fitted onset lines and # the governing level difference. res.plot() plt.show() ```

Two things are worth reading off the plot. The onset lines are the least-squares fits over the detected rise, so a shallow fit on a visibly steep edge means the level history was logged too coarsely to resolve it. And the level difference is measured from the level *just before* the onset, not from the long-term background, which is why a second strike arriving on the decay tail of the first one scores a smaller `LD` than it appears to deserve. Because the onset rate and level difference are level *differences*, the adjustment is insensitive to the absolute calibration of the meter (clause 8); only the reported `LAeq` and the adjusted `LAeq` depend on it. The scope of the document states that the adjustment typically falls between 0,0 dB and 9,0 dB; the formula itself is not capped, so a very sudden, loud impulse can exceed that range. `ImpulsiveSoundResult.plot()` draws the `LpAF` history with the detected onsets, the least-squares onset lines and the governing level difference marked. ## 4. Where the method sits among the standards Rating standards traditionally handle impulsiveness by category, not by measurement. ISO 1996-1:2016 (Table A.1) adds a fixed 5 dB when the source is *regular impulsive* and 12 dB when it is *highly impulsive*, and leaves the choice of category to the assessor's judgement. NT ACOU 112 replaces that judgement with a measurement: the onset rate and level difference of the actual impulses produce a graduated $K_I$ that runs from 0 dB for barely perceptible onsets to about 18 dB for the most sudden, loud ones, crossing the two conventional figures on the way. The approach proved durable: the same onset-based prominence was later carried into ISO/PAS 1996-3:2022, which uses it to decide *objectively* whether a source counts as regular or highly impulsive in the ISO 1996-1 rating. Two practical caveats when applying it. The onset is read from the A-weighted, time-weighting-F level history, and the 125 ms time constant of weighting F itself limits how fast a recorded level can rise: very short impulses arrive already smoothed, so the level history must be logged at the meter's full output rate (not in coarse intervals); otherwise the onset rate, and with it $P$, is underestimated. And the method rates the *prominence* of the impulses, not their energy: $K_I$ rides on top of the measured `LAeq` in the rating level, it never replaces it. ## 5. Assessment report (`.report()`) `ImpulseProminenceResult.report(path)` renders a one-page PDF fiche laid out like an impulsive-sound assessment report of an environmental-noise laboratory, following NT ACOU 112:2002 (carried into ISO/PAS 1996-3:2022): a standard-basis line, an optional metadata header block (source/situation, client, measurement position, instrumentation and date, with the 30-minute assessment period always shown), a full-width per-impulse table (onset rate, level difference, predicted prominence `P` and whether the onset qualifies as an impulse) above the adjustment-curve plot `KI(P)` with the candidate impulses marked, the boxed governing prominence `P` together with the derived `LAeq` adjustment `KI` (Formula 2), an optional PASS/FAIL verdict row and a prominence-category note, and a footer with the fixed disclaimer. It uses the same `ReportMetadata` container (documented under [Field insulation](https://jmrplens.github.io/phonometry/guides/insulation-field/#report-metadata-reportmetadata)) and rendering engine as the [ISO 532-1 loudness fiche](https://jmrplens.github.io/phonometry/guides/loudness/#iso-532-1-report-report); a supplied `requirement` is read as the maximum acceptable governing prominence `P` (a less prominent impulse passes). Rendering needs reportlab (`pip install phonometry[report]`); only `engine="reportlab"` is supported. The fiche renders in English by default; pass `language="es"` for a Spanish fiche (translated fixed strings and a comma decimal separator), e.g. `res.report("impulse_fiche_es.pdf", language="es")`. ```python from phonometry import environmental, ReportMetadata # The three-impulse pile-driving set (onset rate dB/s, level difference dB). res = environmental.impulse_prominence([1200.0, 300.0, 60.0], [32.0, 18.0, 11.0]) res.report( "impulse_fiche.pdf", metadata=ReportMetadata( specimen="Pile-driving site, intermittent hammering", measurement_standard="ISO 1996-2", laboratory="Phonometry Reference Laboratory", requirement=10.0, # maximum acceptable governing prominence P ), ) # governing P and the LAeq adjustment KI (dB) ``` The example fiche, regenerated with `make reports`, is kept rendered in the repository; click the preview to open the PDF. [](https://raw.githubusercontent.com/jmrplens/phonometry/main/.github/reports/ntacou112_impulse_prominence_example.pdf) *Impulsive-sound prominence fiche (`ImpulseProminenceResult.report`), governing prominence P with the LAeq adjustment KI.* ## References - Nordtest. (2002). *Acoustics: Prominence of impulsive sounds and for adjustment of LAeq* (Nordtest Method NT ACOU 112). [nordtest.info](https://www.nordtest.info/wp/2002/05/01/acoustics-prominence-of-impulsive-sounds-and-for-adjustment-of-laeq-nt-acou-112/). The implemented method, freely downloadable from the Nordtest archive. - International Organization for Standardization. (2016). *Acoustics — Description, measurement and assessment of environmental noise — Part 1: Basic quantities and assessment procedures* (ISO 1996-1:2016). [iso.org catalogue](https://www.iso.org/standard/59765.html). The rating framework and the Table A.1 category adjustments (5 dB regular, 12 dB highly impulsive) that the measured prominence calibrates against. - International Organization for Standardization. (2022). *Acoustics — Description, measurement and assessment of environmental noise — Part 3: Objective method for the measurement of prominence of impulsive sounds and for adjustment of LAeq* (ISO/PAS 1996-3:2022). [iso.org catalogue](https://www.iso.org/standard/77035.html). The ISO successor built on the NT ACOU 112 onset-rate prominence. ## Standards NT ACOU 112:2002 (Nordtest), *Prominence of impulsive sounds and for adjustment of LAeq*: the predicted prominence (clause 7, Formula 1), the adjustment to LAeq (clause 8, Formula 2) and the rating level (clause 8, Note 1), with the onset defined in clauses 4.5-4.7.