---
title: "Frequency Weighting (A, B, C, D, G, AU, Z)"
description: "A/C/Z frequency weighting per IEC 61672-1 (class 1, with a high-frequency accuracy mode), G-weighting for infrasound per ISO 7196, the historical B (ANSI S1.4-1983) and D (IEC 537) curves, and AU per IEC 61012 for audible sound in the presence of ultrasound."
references:
  - type: article
    authors: ["Fletcher, H.", "Munson, W. A."]
    year: 1933
    title: "Loudness, its definition, measurement and calculation"
    journal: "The Journal of the Acoustical Society of America"
    volume: 5
    issue: 2
    pages: "82-108"
    doi: "10.1121/1.1915637"
    note: "The original equal-loudness measurements whose 40-phon contour the A-curve inverts (section 1)."
  - type: standard
    organization: "International Organization for Standardization"
    year: 2023
    title: "Acoustics — Normal equal-loudness-level contours"
    designation: "ISO 226:2023"
    url: "https://www.iso.org/standard/83117.html"
    note: "The modern successors of the Fletcher-Munson curves, drawn in the diagram of section 1."
  - type: standard
    organization: "International Electrotechnical Commission"
    year: 2013
    title: "Electroacoustics — Sound level meters — Part 1: Specifications"
    designation: "IEC 61672-1:2013"
    url: "https://webstore.iec.ch/en/publication/5708"
    note: "The normative A, C and Z frequency-weighting curves (the Annex E analytic definition from four corner frequencies, normalized to 0 dB at 1 kHz), the class 1 tolerances the high_accuracy design keeps up to 16 kHz, and the Table 3 class 1/class 2 acceptance limits checked by verify_weighting_class in section 8."
  - type: standard
    organization: "International Organization for Standardization"
    year: 1995
    title: "Acoustics — Frequency-weighting characteristic for infrasound measurements"
    designation: "ISO 7196:1995"
    url: "https://www.iso.org/standard/13813.html"
    note: "The G-weighting pole/zero definition (Table 1), verified against every Table 2 nominal response value (0.25 Hz to 315 Hz)."
  - type: standard
    organization: "American National Standards Institute"
    year: 1983
    title: "Specification for Sound Level Meters"
    designation: "ANSI S1.4-1983"
    note: "The historical B weighting: Appendix C analytic definition (Formula C2), Table IV design goals and Table V tolerance limits checked by verify_weighting_class in section 8."
  - type: standard
    organization: "International Electrotechnical Commission"
    year: 1990
    title: "Filters for the measurement of audible sound in the presence of ultrasound"
    designation: "IEC 61012:1990"
    url: "https://webstore.iec.ch/en/publication/4383"
    note: "The AU weighting: U-weighting pole locations (Table 2), nominal responses and tolerances (Table 1) and the combined AU definition of subclause 2.2."
  - type: standard
    organization: "International Electrotechnical Commission"
    year: 1976
    title: "Frequency weighting for the measurement of aircraft noise (D-weighting)"
    designation: "IEC 537:1976 (withdrawn)"
    note: "The D weighting, implemented from its published rational transfer function and cross-checked against two independent implementations and the tabulated curve republished in NASA CR-3406 (Table SLD-I)."
  - type: report
    authors: ["Bennett, R. L.", "Pearsons, K. S."]
    year: 1981
    title: "Handbook of Aircraft Noise Metrics"
    institution: "NASA"
    number: "NASA CR-3406"
    url: "https://ntrs.nasa.gov/citations/19810013341"
    note: "Republishes the IEC 537 D-weighting table (Table SLD-I) used to pin the D response in CI."
---

import ThemeImage from '../../../components/ThemeImage.astro';

Frequency weighting curves simulate the human ear's sensitivity. A, C and Z
are specified by **IEC 61672-1:2013**; the infrasound G curve is specified by
**ISO 7196:1995**. Three more curves round out the family: the historical
**B** (ANSI S1.4-1983), the withdrawn aircraft-noise **D** (IEC 537) and
**AU** (IEC 61012) for audible sound in the presence of ultrasound
(section 4).

<ThemeImage src="https://raw.githubusercontent.com/jmrplens/phonometry/main/.github/images/weighting_responses.svg" alt="A, B, C, D, AU and Z frequency weighting curves with a zoom showing the positive region of the A curve (+1.27 dB at 2.5 kHz)" width="80%" loading="eager" />

<details>
<summary>Show the code for this figure</summary>

```python
import matplotlib.pyplot as plt
import numpy as np
from phonometry import metrology

# Measure each curve's response: weight a centered unit impulse and take
# its spectrum (1 s buffer -> 1 Hz frequency resolution).
fs = 48000
impulse = np.zeros(fs)
impulse[fs // 2] = 1.0
freqs = np.fft.rfftfreq(fs, 1 / fs)

fig, ax = plt.subplots(figsize=(9, 5))
for curve in ("A", "B", "C", "D", "AU", "Z"):
    spectrum = np.fft.rfft(metrology.weighting_filter(impulse, fs, curve=curve))
    ax.semilogx(freqs[1:], 20 * np.log10(np.abs(spectrum[1:]) + np.finfo(float).eps),
                label=curve)
ax.set(xlim=(10, 20000), ylim=(-80, 15),
       xlabel="Frequency [Hz]", ylabel="Response [dB]")
ax.grid(True, which="both", alpha=0.3)
ax.legend()
plt.show()
```

</details>

* **A-Weighting (`A`):** Standard for environmental noise (IEC 61672-1).
* **C-Weighting (`C`):** Used for peak sound pressure and high-level noise.
* **Z-Weighting (`Z`):** Zero weighting, completely flat response.
* **G-Weighting (`G`):** Infrasound weighting per ISO 7196 (see below).
* **B-Weighting (`B`):** Historical middle curve of ANSI S1.4-1983 (section 4).
* **D-Weighting (`D`):** Aircraft-noise weighting of the withdrawn IEC 537 (section 4).
* **AU-Weighting (`AU`):** A-weighting with the IEC 61012 ultrasound cutoff (section 4).

## 1. Where the curves come from

The A and C curves are inverted equal-loudness contours, frozen into filters:
**A** approximates the inverse of the historic 40-phon contour (quiet levels,
where the ear discards bass most aggressively) and **C** the flatter ~100-phon
one (loud levels). IEC 61672-1:2013 (Annex E) defines both analytically from
four corner frequencies:

$$
f_1 = 20.599\ \text{Hz}, \quad f_2 = 107.653\ \text{Hz}, \quad
f_3 = 737.862\ \text{Hz}, \quad f_4 = 12194.217\ \text{Hz}
$$

C is a band-pass with double poles at $f_1$ and $f_4$ (2 zeros at the origin);
A adds the $f_2$ and $f_3$ poles (4 zeros), which is why it keeps falling
through the low-mids. Both are normalized to exactly 0 dB at 1 kHz. Z is the
absence of weighting. The full pole/zero derivation is in the
[Theory](/phonometry/reference/theory/signal-analysis/) page.

<ThemeImage src="https://raw.githubusercontent.com/jmrplens/phonometry/main/.github/images/diagram_equal_loudness_weighting.svg" alt="Equal-loudness contours per ISO 226 on the left, with the 40-phon contour highlighted; on the right the A-weighting curve overlaid on the inverted 40-phon contour, showing that A is the flipped contour frozen into a realizable filter" width="92%" />

### A short history: A, B, C and Z

The chain runs from Fletcher and Munson's 1933 equal-loudness measurements
to the first American sound level meter standard (1936), which gave meters
switchable responses so the reading could approximate loudness at different
levels: **A** from the 40-phon contour for quiet sounds, **B** from the
~70-phon contour for moderate ones, and a flat response for loud ones (the
**C** curve proper, mirroring the flatter ~100-phon contour, arrived with
the 1944 revision). Switching curves by level died in practice (readings jumped at the
switch points, and field measurements became incomparable), but A survived
alone: decades of hearing-damage and community-annoyance data had been
collected with it, and it correlates with both about as well as far more
elaborate metrics. IEC 61672-1 (first edition 2002) finished the cleanup:
B was dropped, A and C were kept with tightened tolerances, and **Z** was
introduced to replace the vaguely specified "linear" of older meters, which
varied by manufacturer. The B curve (and the aircraft-noise D curve that met
the same fate) remains available for historical data; see section 4.

### When C − A matters

Because A discards bass and C keeps it, the difference
$L_{Ceq} - L_{Aeq}$ is a one-number indicator of low-frequency content:

- **Below about 10 dB**: an ordinary broadband spectrum; the A-weighted
  level rates it fairly.
- **Around 15 to 20 dB or more**: the energy is concentrated at low
  frequencies (HVAC rumble, compressors, music bass through a wall). The
  A-weighted level then understates the problem; look at the octave
  spectrum, and below 20 Hz switch to the G curve.
- **Hearing-protector selection**: the HML method of ISO 4869-2 keys on
  exactly this C-minus-A difference to decide how much low-frequency
  attenuation a protector must provide (the simpler SNR method sidesteps it
  by working from the C-weighted level directly).

```python
import numpy as np
from phonometry import metrology

# A 50 Hz rumble under a light broadband hiss: quiet in A, loud in C.
fs = 48000
t = np.arange(10 * fs) / fs
rng = np.random.default_rng(1)
x = 0.2 * np.sin(2 * np.pi * 50 * t) + 0.01 * rng.standard_normal(t.size)

la = metrology.leq(metrology.weighting_filter(x, fs, curve="A"))
lc = metrology.leq(metrology.weighting_filter(x, fs, curve="C"))
print(f"LAeq = {la:.1f} dB   LCeq = {lc:.1f} dB   C - A = {lc - la:.1f} dB")
# LAeq = 52.4 dB   LCeq = 75.7 dB   C - A = 23.2 dB
# C - A above 20 dB: the A-weighted number alone would hide the rumble.
```

## 2. Basic usage

```python
import numpy as np
from phonometry import metrology

# recording: a calibrated microphone capture (Pa) — recorded through your measurement chain. Synthesized here so the guide runs standalone.
fs = 48000
recording = 0.2 * np.sin(2 * np.pi * 1000 * np.arange(fs) / fs)

# Apply A-weighting to the raw recording
weighted_signal = metrology.weighting_filter(recording, fs, curve='A')

# Apply C-weighting for peak analysis
c_weighted_signal = metrology.weighting_filter(recording, fs, curve='C')
```

## 3. Infrasound: G-weighting (ISO 7196)

The **G frequency weighting** (ISO 7196:1995) rates infrasound the way A-weighting
rates audible noise. It is defined by a pole-zero configuration with 0 dB gain at
10 Hz, rises at 12 dB/octave from 1 Hz to 20 Hz (matching the steep growth of
perception in that band) and falls off at 24 dB/octave outside it. Use it for
sources with significant energy below 20 Hz (wind turbines, HVAC, blasting):

```python
import numpy as np
from phonometry import metrology

# recording: a calibrated microphone capture (Pa) — recorded through your measurement chain. Synthesized here so the guide runs standalone.
fs = 48000
recording = 0.2 * np.sin(2 * np.pi * 1000 * np.arange(fs) / fs)

g_weighted = metrology.weighting_filter(recording, fs, curve='G')
```

<ThemeImage src="https://raw.githubusercontent.com/jmrplens/phonometry/main/.github/images/g_weighting_response.svg" alt="G-weighting frequency response from 0.1 Hz to 1 kHz with the ISO 7196 Table 2 nominal values overlaid" width="80%" />

<details>
<summary>Show the code for this figure</summary>

```python
import matplotlib.pyplot as plt
import numpy as np
from phonometry import metrology

# Measure the G response: weight a centered unit impulse and take its
# spectrum. A long buffer gives the resolution the infrasound range
# needs (20 s -> 0.05 Hz).
fs = 4000
impulse = np.zeros(20 * fs)
impulse[impulse.size // 2] = 1.0
freqs = np.fft.rfftfreq(impulse.size, 1 / fs)
spectrum = np.fft.rfft(metrology.weighting_filter(impulse, fs, curve="G"))

fig, ax = plt.subplots(figsize=(9, 5))
ax.semilogx(freqs[1:],
            20 * np.log10(np.abs(spectrum[1:]) + np.finfo(float).eps))
ax.plot(10, 0, "o", color="tab:red", label="0 dB at 10 Hz")
ax.set(xlim=(0.1, 1000), ylim=(-90, 15),
       xlabel="Frequency [Hz]", ylabel="G-weighting response [dB]")
ax.grid(True, which="both", alpha=0.3)
ax.legend()
plt.show()
```

</details>

The implementation follows the ISO 7196 Table 1 pole/zero values exactly and is
verified in CI against every Table 2 nominal response value (0.25 Hz to 315 Hz).
`WeightingFilter(fs, "G")` supports the same multichannel and stateful block
processing as A/C. Levels measured with the G curve are reported as
L<sub>pG</sub> (or L<sub>Geq</sub> for the equivalent level over time).

## 4. Historical and special-purpose curves: B, D and AU

Three more curves complete the family. All three share the machinery of the
IEC 61672-1 curves (0 dB at 1 kHz, `high_accuracy` oversampling, multichannel
and stateful block processing).

### B (ANSI S1.4-1983, historical)

The middle curve of the original A/B/C level-switching scheme, drawn from
the ~70-phon equal-loudness contour. Analytically it is the C weighting with
one more zero at the origin and one extra real pole at
$f_5 = 158.49\ \text{Hz}$ (Appendix C of ANSI S1.4-1983), so it discards
less bass than A and more than C. It was dropped when IEC 61672-1 replaced
the older sound-level-meter standards; use it only to reproduce historical
data and measurements taken under older national codes (some legacy
automotive test procedures reported dB(B)). The implementation follows the
ANSI S1.4-1983 Appendix C constants and is pinned in CI against the Table IV
response values, within the strictest Table V mask (Type 0).

### D (IEC 537, withdrawn: aircraft noise)

The D weighting approximated the *perceived noisiness* contours used by the
perceived-noise-level (PNL) rating, so a plain sound level meter could
estimate aircraft noise: the +11.5 dB hump around 3.15 kHz is where jet
turbomachinery whine annoys most (it is deliberately *not* an equal-loudness
feature). NASA's aircraft-noise handbook gives the classic rule of thumb
$L_{PN} \approx L_D + 7\ \text{dB}$. IEC 537 was withdrawn and current
certification practice reports EPNL from one-third-octave analysis or plain
A-weighted levels, so `D` is provided for historical data and comparisons.
With the standard unavailable, the implementation uses the widely published
IEC 537 rational transfer function and is cross-checked against two
independent implementations (SQAT's zeros/poles and librosa's closed form,
which agree within 0.002 dB) and pinned in CI against the IEC 537 table
republished in NASA CR-3406.

```python
import numpy as np
from phonometry import metrology

# A 3.15 kHz whine sits right on the D-weighting hump: D rates it
# 10 dB *louder* than A does.
fs = 96000
t = np.arange(fs) / fs
whine = 0.1 * np.sin(2 * np.pi * 3150 * t)

ld = metrology.leq(metrology.weighting_filter(whine, fs, curve="D"))
la = metrology.leq(metrology.weighting_filter(whine, fs, curve="A"))
print(f"LD = {ld:.1f} dB   LA = {la:.1f} dB")
# LD = 82.5 dB   LA = 72.2 dB
```

### AU (IEC 61012, current: audible sound in the presence of ultrasound)

The only one of the three still in force. `AU` is the A weighting cascaded
with the **U** low-pass filter of IEC 61012:1990 (six poles, Table 2): flat
relative to A up to 10 kHz, then a steep cutoff (-13 dB at 16 kHz, -61.8 dB
at 40 kHz for U alone). Use it when strong ultrasonic components (ultrasonic
cleaners and welders, rodent repellers, some public-space deterrents) would
otherwise leak into an A-weighted reading through the meter's imperfect
high-frequency roll-off and overstate the *audible* exposure:

```python
import numpy as np
from phonometry import metrology

# 1 kHz tone (audible) buried under a strong 25 kHz ultrasonic component.
fs = 96000
t = np.arange(fs) / fs
audible = 0.1 * np.sin(2 * np.pi * 1000 * t)
x = audible + 1.0 * np.sin(2 * np.pi * 25000 * t)

la = metrology.leq(metrology.weighting_filter(x, fs, curve="A"))
lau = metrology.leq(metrology.weighting_filter(x, fs, curve="AU"))
la_ref = metrology.leq(metrology.weighting_filter(audible, fs, curve="A"))
print(f"LA = {la:.1f} dB   LAU = {lau:.1f} dB   audible alone = {la_ref:.1f} dB")
# LA = 78.6 dB   LAU = 71.0 dB   audible alone = 71.0 dB
# The ultrasound inflates LA by 7.6 dB; AU recovers the audible level.
```

Ultrasound only reaches a digital filter when the sample rate captures it,
so measure at 96 kHz or more (at 48 kHz there is nothing above 24 kHz to
reject); the AU design internally oversamples toward 288 kHz to keep the
steep U roll-off accurate. Levels are reported as L<sub>AU</sub>. The
implementation follows the Table 2 pole locations exactly (they reproduce
every Table 1 nominal value within 0.05 dB) and is verified in CI against
the Table 1 tolerances up to 40 kHz.

## 5. `weighting_filter()` / `WeightingFilter` parameters

| Parameter | Type | Units | Range / default | Notes |
| :--- | :--- | :--- | :--- | :--- |
| `x` | 1D or 2D array | any | non-empty | 2D is `[channels, samples]` |
| `fs` | int | Hz | > 0 | |
| `curve` | str | — | `'A'` (default), `'B'`, `'C'`, `'D'`, `'G'`, `'AU'`, `'Z'` | `'G'` per ISO 7196 (infrasound); `'B'`/`'D'` historical (§4); `'AU'` per IEC 61012 (§4); `'Z'` is a bypass |
| `high_accuracy` | bool | — | default `True` (function); class default `None` resolves to `not stateful` | Internal oversampling keeps A/C in class 1 up to 16 kHz; details in §7 |
| `stateful` | bool (class only) | — | default `False` | Carries filter state across blocks (streaming) |
| `steady_ic` | bool (class only) | — | default `False` | Steady-state initial conditions (no onset transient) |

## 6. Reusable filter object

If you weight many signals with the same parameters, design the filter once:

```python
import numpy as np
from phonometry import metrology

# recording: a calibrated microphone capture (Pa) — recorded through your measurement chain. Synthesized here so the guide runs standalone.
fs = 48000
recording = 0.2 * np.sin(2 * np.pi * 1000 * np.arange(fs) / fs)

wf = metrology.WeightingFilter(fs, "A")
signals = [recording]                # your batch of recordings
for recording in signals:
    weighted = wf.filter(recording)
```

## 7. High-frequency accuracy (`high_accuracy`)

A plain bilinear-transform design compresses the response near Nyquist: at
fs = 48 kHz the A-curve error at 12.5 kHz reaches −2.7 dB, outside the IEC
61672-1 **class 1** tolerance (+2.0/−2.5 dB).

By default (`high_accuracy=True`), phonometry designs and runs the weighting
filter at an internally oversampled rate (up to 8×, reaching ≥ 144 kHz at
common audio rates; a 96 kHz input runs ×2) and decimates back, keeping
the response within class 1 tolerances up to 16 kHz (error ≈ −0.5 dB at
12.5 kHz for fs = 48 kHz).

<ThemeImage src="https://raw.githubusercontent.com/jmrplens/phonometry/main/.github/images/weighting_accuracy_hf.svg" alt="A-weighting high-frequency accuracy at 48 kHz: analytic curve versus plain bilinear versus oversampled design, with error subplot" width="80%" />

*The plain bilinear design (red) crosses the class 1 tolerance near 12.5 kHz;
the oversampled design (blue) stays close to the analytic curve.*

<details>
<summary>Show the code for this figure</summary>

```python
import matplotlib.pyplot as plt
import numpy as np
from phonometry import metrology

# Measured response of both designs at fs = 48 kHz: weight a centered
# unit impulse and take its spectrum...
fs = 48000
impulse = np.zeros(fs)
impulse[fs // 2] = 1.0
freqs = np.fft.rfftfreq(fs, 1 / fs)[1:]

# ...versus the analytic IEC 61672-1 A-curve built from the four corner
# frequencies of section 1, normalized to 0 dB at 1 kHz.
f1, f2, f3, f4 = 20.599, 107.653, 737.862, 12194.217
gain = (f4**2 * freqs**4) / ((freqs**2 + f1**2)
        * np.sqrt((freqs**2 + f2**2) * (freqs**2 + f3**2))
        * (freqs**2 + f4**2))
analytic = 20 * np.log10(gain / gain[np.argmin(np.abs(freqs - 1000))])

fig, ax = plt.subplots(figsize=(9, 5))
ax.semilogx(freqs, analytic, "k--", label="Analytic (IEC 61672-1)")
for high_accuracy, label in ((False, "Plain bilinear"),
                             (True, "Oversampled (default)")):
    weighted = metrology.weighting_filter(impulse, fs, curve="A",
                                high_accuracy=high_accuracy)
    response = 20 * np.log10(np.abs(np.fft.rfft(weighted))
                             + np.finfo(float).eps)[1:]
    ax.semilogx(freqs, response, label=label)
ax.set(xlim=(1000, 20000), ylim=(-12, 3),
       xlabel="Frequency [Hz]", ylabel="A-weighting response [dB]")
ax.grid(True, which="both", alpha=0.3)
ax.legend()
plt.show()
```

</details>

- `high_accuracy=False` restores the legacy plain-bilinear behavior.
- For `'G'` the flag is silently ignored: its 0.25–315 Hz range is already
  exact with the plain design.
- **Stateful (block) processing** always uses the legacy design: the internal
  FIR resampling is incompatible with block continuity. Passing
  `high_accuracy=True` together with `stateful=True` raises a `ValueError`.

```python
import numpy as np
from phonometry import metrology

# recording: a calibrated microphone capture (Pa) — recorded through your measurement chain. Synthesized here so the guide runs standalone.
fs = 48000
recording = 0.2 * np.sin(2 * np.pi * 1000 * np.arange(fs) / fs)

# Explicit legacy behavior
y = metrology.weighting_filter(recording, fs, curve="A", high_accuracy=False)

# Stateful block processing (legacy design, state carried between blocks)
wf = metrology.WeightingFilter(fs, "A", stateful=True)
blocks = [recording]                 # your sequence of recording blocks
for block in blocks:
    weighted = wf.filter(block)
```

See [Block Processing](/phonometry/guides/block-processing/) for the streaming workflow and
[Theory](/phonometry/reference/theory/signal-analysis/) for the analytic curve definitions.

## 8. Verifying against the tolerance tables (IEC 61672-1, ANSI S1.4, IEC 61012)

`verify_weighting_class` checks a weighting filter against the acceptance
limits of **IEC 61672-1:2013** (Table 3). It evaluates the filter's relative
response at the *exact* base-10 frequency behind each nominal label below
Nyquist (Table 3's design goals are computed at $f = 1000 \cdot 10^{n/10}$,
e.g. 15 848.9 Hz for "16 kHz"; IEC 61672-3 tests at the same frequencies),
subtracts the design-goal weighting, and reports the performance class per
frequency with its margin in dB. A dense logarithmic sweep additionally
enforces subclause 5.5.7 *between* the nominal frequencies (the deviation
from the analytic Annex E goal must stay within the larger of the two
adjacent limits, so a resonance or notch between nominals cannot pass), and
when Table 3 rows with finite lower limits fall beyond Nyquist the verdict is
flagged `range_limited` (it then attests the checked frequencies only, not
full 10 Hz-20 kHz conformance):

```python
from phonometry import metrology

result = metrology.verify_weighting_class(metrology.WeightingFilter(48000, "A"))
print(result["overall_class"])          # 1
print(result["range_limited"])          # False
print(result["between_nominals"])       # {'worst_freq': ..., 'margin_class1_db': ...}
print(result["bands"][20])
# {'freq': 1000.0, 'class': 1, 'deviation_db': 0.0, 'margin_class1_db': 0.7, 'margin_class2_db': 1.0}
```

The Table 3 acceptance mask itself is public too: `weighting_class_limits(1)`
returns the 34 nominal frequencies with the lower/upper deviation limits (a
lower limit of `-inf` means only the upper limit applies). The limits qualify
the *deviation* from the design goal, so they are the same for A, C and Z.

The same verifier covers the section 4 curves that have published tolerance
tables. For `B` it uses ANSI S1.4-1983 (Table IV design goals, Table V
limits) and the "class" verdicts read as the standard's instrument **Types**
1 and 2. For `AU` it uses IEC 61012:1990 Table 1 (nominal A + nominal U with
the separate-unit tolerances, zero at the 1 kHz reference); IEC 61012
publishes a single tolerance set, so both margin slots agree and the verdict
is simply complies (1) or not (`None`) — note that checking the rows above
20 kHz needs `fs` ≥ 96 kHz (below that they are dropped and the verdict is
`range_limited`). `G` and `D` are rejected: ISO 7196 defines one ±1 dB
tolerance with no class structure, and the withdrawn IEC 537 left no
tolerance table behind (both curves are pinned numerically in the CI
conformance report instead).

<ThemeImage src="https://raw.githubusercontent.com/jmrplens/phonometry/main/.github/images/weighting_class_mask.svg" alt="A and C weighting deviations at 48 kHz threading within the IEC 61672-1 Table 3 class 1 acceptance corridor, with the wider class 2 limits dotted" width="80%" />

*The oversampled A and C designs (blue, purple) stay near zero deviation,
well inside the class 1 corridor (shaded); the wider class 2 limits are
dotted. The corridor widens at the band extremes where only a one-sided
limit applies.*

<details>
<summary>Show the code for this figure</summary>

```python
import matplotlib.pyplot as plt
import numpy as np
from phonometry import metrology

freqs, lower1, upper1 = metrology.weighting_class_limits(1)
_, lower2, upper2 = metrology.weighting_class_limits(2)
lo1, lo2 = np.clip(lower1, -7, 7), np.clip(lower2, -7, 7)

fig, ax = plt.subplots(figsize=(10, 6.5))
ax.fill_between(freqs, lo1, upper1, step="mid", alpha=0.10,
                label="Class 1 acceptance region")
ax.plot(freqs, upper1, drawstyle="steps-mid", label="Class 1 upper/lower limit")
ax.plot(freqs, lo1, drawstyle="steps-mid", color="C1")
ax.plot(freqs, upper2, ":", drawstyle="steps-mid", label="Class 2 upper/lower limit")
ax.plot(freqs, lo2, ":", drawstyle="steps-mid", color="C2")

for curve, marker in (("A", "o"), ("C", "s")):
    bands = metrology.verify_weighting_class(metrology.WeightingFilter(48000, curve))["bands"]
    f = [b["freq"] for b in bands]
    dev = [b["deviation_db"] for b in bands]
    ax.plot(f, dev, marker=marker, label=f"{curve} weighting deviation (48 kHz)")

ax.set(xscale="log", xlim=(10, 20000), ylim=(-7, 7),
       xlabel="Frequency [Hz]", ylabel="Deviation from design goal [dB]")
ax.legend(fontsize=8, ncol=2)
plt.show()
```

</details>

## See also

- API reference: [`metrology.parametric_filters`](/phonometry/reference/api/filters/parametric-filters/) and [`metrology.compliance`](/phonometry/reference/api/filters/compliance/).
