Source: https://jmrplens.github.io/phonometry/guides/noise-induced-hearing-loss/ # Noise-induced hearing loss (ISO 1999) **ISO 1999:2013** estimates the hearing loss a population suffers from occupational noise. It gives the **noise-induced permanent threshold shift** (NIPTS), the extra hearing loss caused by the noise, on top of ageing, as a function of the exposure level, the exposure duration and the audiometric frequency, together with its spread across a population. It then combines the noise component with the age component (the ISO 7029 threshold, "database A") into the **hearing threshold level associated with age and noise** (HTLAN). Both are defined at the six audiometric frequencies 500 Hz to 6000 Hz, where noise damage concentrates (the characteristic 4 kHz notch). ## 1. Noise-induced permanent threshold shift (clause 6.3) The median NIPTS for exposure durations of 10 to 40 years grows with the square of the excess of the noise exposure level over a frequency-dependent cut-off $L_0$ (ISO 1999 clause 6.3.1, Formula 2, Table 1): $$ N_{50} = \left[u + v\,\lg\!\frac{t}{t_0}\right](L_{EX,8h} - L_0)^2, $$ with $t$ the exposure in years and $t_0 = 1$ year; below $L_0$ the effect is zero. A population **fractile** follows from two half-Gaussians whose spreads $d_u$ (worse than the median) and $d_l$ (better) are given by Formulae 6/7 and Table 3: $N_{50} + z\,d$ with $z$ the standard-normal quantile, clamped at zero (clause 6.3.2). The `fractile` argument is the fraction of the population *below* the returned value, so `fractile=0.9` is the most-susceptible tenth. Note that ISO 1999 writes its own percentage $Q$ the other way round: in Formulae (4)/(5) $Q$ is the percentage with *worse* hearing, so that same tenth is $Q = 10\;\%$, which is how the `.report()` fiche prints it and how the Annex D columns are headed. ```python from phonometry import hearing # Median NIPTS after 20 years at an 8 h-normalised level of 90 dB(A). r = hearing.nipts(90.0, 20.0, fractile=0.5) print(r.frequencies.astype(int)) # [ 500 1000 2000 3000 4000 6000] print(r.median.round(1)) # [ 0. 0.1 4.1 10.2 12.9 8.5] # The most-susceptible tenth of the population (90th percentile): print(hearing.nipts(90.0, 20.0, fractile=0.9).value.round(1)) # [ 0. 0.1 7.7 16.2 17.8 13.6] r.plot() # the NIPTS spectrum with its fractile band (needs matplotlib) ``` The shift peaks near 4 kHz and deepens with both level and duration. Below the cut-off (here 500 Hz, $L_0 = 93$ dB, and 1000 Hz, $L_0 = 89$ dB, at 90 dB) the noise causes no permanent shift. For durations under 10 years the median is extrapolated from the 10-year value (Formula 3); a subset of the frequencies can be requested with `frequencies=`. A longer, louder career makes the shape unmistakable:

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```python import matplotlib.pyplot as plt from phonometry import hearing # 40 years at an 8 h-normalised 95 dB(A), most-susceptible tenth. res = hearing.nipts(95.0, 40.0, fractile=0.9) print(res.median.round(1)) # the median N50 print(res.value.round(1)) # the 90 % fractile # One line: the median, the fractile and the 10-90 % band. res.plot() plt.show() ```

The notch at 3 kHz to 4 kHz, with partial recovery at 6 kHz, is the signature of noise damage and the reason audiometric surveillance tests those frequencies. It comes out of the model rather than being imposed on it: the cut-off level $L_0$ and the coefficients $u$, $v$ of Table 1 are lowest where the ear is most vulnerable, so the same $L_{EX,8h}$ buys far more shift at 4 kHz than at 500 Hz. ## 2. Age and noise combined: HTLAN (clause 6.1) The noise component does not simply add to the age component: ISO 1999 Formula (1) combines them with a compression term that matters once the total exceeds about 40 dB: $$ H' = H + N - \frac{H\,N}{120}, $$ where $H$ is the age threshold (HTLA, from ISO 7029 at the same fractile) and $N$ the NIPTS. `htlan` evaluates both components and their combination. ```python from phonometry import hearing # A 60-year-old man, 30 years at 95 dB(A), median. h = hearing.htlan(60, "male", 95.0, 30.0, fractile=0.5) print(h.htla.round(1)) # [ 6. 7.8 12.5 16.6 20.2 25.9] age alone print(h.nipts.round(1)) # [ 0.5 3. 11.8 21.6 24.8 17.6] noise alone print(h.threshold.round(1)) # [ 6.5 10.7 23. 35.2 40.8 39.8] age + noise h.plot() # the three curves of the figure below: HTLA, NIPTS and HTLAN ``` At 4 kHz the age component (20.2 dB) and the noise component (24.8 dB) combine to 40.8 dB rather than their 45.0 dB sum; the compression term removes 4.2 dB.

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```python import matplotlib.pyplot as plt from phonometry import hearing from phonometry.hearing.noise_induced_hearing_loss import NIPTS_FREQUENCIES as f # One line for the NIPTS spectrum with its fractile band: hearing.nipts(95.0, 40.0, 0.9).plot() plt.show() # By hand, both panels: fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12.5, 5.6)) for yr in (10, 20, 30, 40): ax1.plot(f, hearing.nipts(95.0, yr, 0.5).median, "o-", label=f"{yr} yr") ax1.set_xscale("log"); ax1.invert_yaxis(); ax1.legend() h = hearing.htlan(60, "male", 95.0, 30.0, 0.5) ax2.plot(f, h.htla, "o-", label="Age (HTLA)") ax2.plot(f, h.nipts, "^-", label="Noise (NIPTS)") ax2.plot(f, h.threshold, "s--", label="Age + noise (HTLAN)") ax2.set_xscale("log"); ax2.invert_yaxis(); ax2.legend() plt.show() ```

**Three quantities, kept distinct.** It is worth being precise about what each symbol means, because they are easy to conflate: - **NIPTS** ($N$) is the *noise-induced permanent threshold shift*: the extra loss the noise causes, and nothing else. It is what an ideal hearing- conservation programme would prevent. On its own it is not an audiogram. - **HTLA** ($H$) is the *age* threshold alone (the ISO 7029 component of the [hearing-threshold](https://jmrplens.github.io/phonometry/guides/hearing-threshold/) guide), the loss the same person would have with no occupational noise. - **HTLAN** ($H'$) is the *total* threshold: age **and** noise combined by Formula (1). This is what an audiometer measures, and the only one of the three you can compare against a real audiogram. The non-linear combination $H' = H + N - HN/120$ is why you cannot simply read NIPTS off a measured audiogram by subtracting an age table: near-total losses would otherwise exceed the ~120 dB physiological ceiling, so the standard compresses the sum. Because the correction $HN/120$ scales with the *product* of the two components, it can already shave a few dB while $H + N$ is below 40 dB; 40 dB is only a rough marker for where the effect becomes noticeable, not a hard boundary, and the correction grows steadily in the regime of a long, loud exposure where both terms are large. **Fence caveats.** ISO 1999 gives thresholds and their distribution; it does *not* define a "hearing handicap" or a compensable fence. A **fence** is a policy line (commonly a 25 dB average over selected frequencies) above which hearing is deemed impaired, and it lives in national regulation, not in this standard. Two cautions follow. First, the fence frequencies matter: averaging over 0.5/1/2 kHz (a classic speech fence) barely sees the 3–6 kHz noise notch, so it under-reports early noise damage that a 3/4/6 kHz average would catch. Second, NIPTS and HTLAN answer different fence questions: NIPTS asks "how much did the noise add", HTLAN "is this ear, age included, over the line"; and a population's *percentage beyond fence* depends on the fractile spread, not just the median, so it must be read from the distribution, never from the median threshold alone. The `NiptsResult` carries the `median` (`N50`), the `spread_upper`/`spread_lower` and the `value` at the requested fractile; the `HtlanResult` carries `htla`, `nipts` and the combined `threshold`. Both expose `.plot()`. The age component alone is the subject of the [hearing-threshold](https://jmrplens.github.io/phonometry/guides/hearing-threshold/) guide. ## References - International Organization for Standardization. (2013). *Acoustics — Estimation of noise-induced hearing loss* (ISO 1999:2013). [iso.org catalogue](https://www.iso.org/standard/45103.html). The implemented model: the median NIPTS, its statistical distribution and the HTLAN combination, with the Annex D worked examples used to validate it. - Passchier-Vermeer, W. (1974). Hearing loss due to continuous exposure to steady-state broad-band noise. *The Journal of the Acoustical Society of America*, 56(5), 1585–1593. [doi:10.1121/1.1903482](https://doi.org/10.1121/1.1903482). One of the field studies whose exposure-response data underpin the NIPTS relations later codified in ISO 1999. - National Institute for Occupational Safety and Health. (1998). *Criteria for a recommended standard: Occupational noise exposure — Revised criteria 1998* (DHHS/NIOSH Publication No. 98-126). [doi:10.26616/NIOSHPUB98126](https://doi.org/10.26616/NIOSHPUB98126), [free PDF](https://www.cdc.gov/niosh/docs/98-126/pdfs/98-126.pdf). A freely available criteria document reviewing the same exposure-response evidence and the fence and hearing-conservation issues discussed above. ## Standards ISO 1999:2013, *Acoustics — Estimation of noise-induced hearing loss*: the HTLAN combination (clause 6.1, Formula 1), the median NIPTS (clause 6.3.1, Formulae 2-3, Table 1) and its statistical distribution (clause 6.3.2, Formulae 4-7, Tables 2-3), validated against the worked examples of Annex D. The age component (database A) is ISO 7029:2017.