Source: https://jmrplens.github.io/phonometry/reference/api/psychoacoustics/loudness-moore-glasberg-time/ Time-varying loudness per ISO 532-3:2023 (Moore-Glasberg-Schlittenlacher). Clean-room implementation of the time-varying loudness model of ISO 532-3:2023, the extension of the stationary Moore-Glasberg method of ISO 532-2:2017 to sounds whose loudness changes over time. The procedure of Clause 7 turns a calibrated pressure waveform into two time histories: * the **short-term loudness** `S'(t)` in sone (clause 7.8), the momentary loudness of a short segment such as a spoken word or a musical note; and * the **long-term loudness** `S''(t)` in sone (clause 7.9), the loudness of a longer segment such as a whole sentence or musical phrase. The signal chain is: * a running short-term spectrum computed from six parallel Hann-windowed FFTs whose segment durations (2, 4, 8, 16, 32 and 64 ms) trade temporal against spectral resolution, each contributing only its own frequency range, updated every 1 ms (clause 7.3); * the level-dependent rounded-exponential excitation pattern of ISO 532-2 sampled on the ERB-number grid from 1.75 Cam to 39 Cam in 0.25 Cam steps (clause 7.4, Formulae 1-6); * the compressive transformation of excitation into instantaneous specific loudness `N'(i)` in sone/Cam (clause 7.5, Formulae 7-9, Tables 2-4, with `C = 0.063` sone/Cam); * an attack/release temporal smoothing of the specific loudness at every centre frequency to the short-term specific loudness (clause 7.6, Formulae 10-13); * the across-frequency smoothing and binaural inhibition of ISO 532-2 (clause 7.7, Formulae 14-17), and integration over the ERB-number scale to the short-term loudness of each ear and their binaural sum (clause 7.8); * a slower attack/release temporal smoothing of the short-term loudness to the long-term loudness (clause 7.9, Formulae 18-21). The loudness of a sound lasting up to about 5 s is well predicted by the maximum of the long-term loudness (clause 7.9); this `n_max` and the corresponding loudness level in phon (Table 5) are the headline results the standard asks to report (clause 9). A steady 1 kHz tone at 40 dB SPL presented binaurally in a free field yields a long-term loudness of 1.000 sone (40 phon) by definition of the sone; the additive spectral calibration of clause 7.3 (nominally +3.32 dB per component) is set so this anchor holds exactly. **Conformance mode for the sampling rate.** Clause 5 prescribes converting the input to 32 kHz and clause 7.3 uses fixed 2048-point FFTs; this implementation deliberately processes at the **native sampling rate** and grows the FFT to `max(2048, next_pow2(segment))` so the 64 ms Hann window is never truncated at 44.1/48 kHz (see `test_low_freq_band_not_truncated_across_sample_rates`). The spectral calibration `_SPECTRAL_CAL_DB` was fixed against the 32 kHz anchor; at other rates the Annex C.1 anchor reproduces with a 0.3 % cross-rate spread (a 0.5 s tone reads `n_max` = 0.9918 / 0.9926 / 0.9900 at 32 / 44.1 / 48 kHz; a settled 1.3 s tone 1.0000 / 1.0000 / 0.9982), far inside the standard's 2.8 phon expanded uncertainty. These values are pinned by the test suite; resample to 32 kHz first if letter-of-standard processing is required. > Auto-generated from the source docstrings by `scripts/generate_api_docs.py` (`make api-docs`). Do not edit by hand. ## loudness_moore_glasberg_time ```python loudness_moore_glasberg_time( signal: Sequence[float] | np.ndarray, fs: float, *, field: Literal['free', 'diffuse', 'eardrum'] = 'free', presentation: Literal['binaural', 'diotic', 'monaural'] = 'binaural', percentiles: Sequence[float] = (1.0, 5.0, 10.0, 50.0, 90.0, 95.0), ) -> MooreGlasbergTimeVaryingLoudness ``` Time-varying loudness of a calibrated pressure signal (ISO 532-3:2023). Implements the Moore-Glasberg-Schlittenlacher method: a running short-term spectrum (six parallel FFTs, clause 7.3) feeds the ISO 532-2 excitation and specific-loudness model (clauses 7.4, 7.5), which is integrated over time to the short-term loudness `S'(t)` (clauses 7.6-7.8) and long-term loudness `S''(t)` (clause 7.9). The signal must be calibrated so its samples are the instantaneous sound pressure in pascals. **Parameters** | Name | Description | | :--- | :--- | | `signal` | Calibrated pressure signal in pascals. A 1-D array is treated as diotic (the same sound at both ears) for a binaural/diotic presentation, or as the single active ear for a monaural presentation; a two-channel `(n, 2)` array gives the left and right ear signals. | | `fs` | Sampling rate in Hz (positive). | | `field` | Listening condition setting the outer-ear transfer: `"free"` (frontal free field, default), `"diffuse"` (diffuse field) or `"eardrum"` (levels already at the tympanic membrane). | | `presentation` | `"binaural"`/`"diotic"` (default) or `"monaural"`. | | `percentiles` | Fractions (percent) for which the exceeded long-term loudness is reported. | **Returns:** A [`MooreGlasbergTimeVaryingLoudness`](/phonometry/reference/api/psychoacoustics/loudness-moore-glasberg-time/#mooreglasbergtimevaryingloudness). A steady 1 kHz tone at 40 dB SPL, binaural, free field, yields a peak long-term loudness of 1.000 sone (40 phon) by definition of the sone. ## MooreGlasbergTimeVaryingLoudness ```python MooreGlasbergTimeVaryingLoudness( time: np.ndarray, short_term_loudness: np.ndarray, long_term_loudness: np.ndarray, short_term_loudness_level: np.ndarray, long_term_loudness_level: np.ndarray, n_max: float, loudness_level_max: float, percentiles: dict[float, float], field: str, presentation: str, ) ``` Result of an ISO 532-3:2023 time-varying loudness calculation. `time` is the frame time axis in seconds (1 ms spacing, clause 7.3). `short_term_loudness` and `long_term_loudness` are the binaural short-term `S'(t)` (clause 7.8) and long-term `S''(t)` (clause 7.9) loudness traces in sone; `short_term_loudness_level` and `long_term_loudness_level` are the corresponding loudness levels in phon (Table 5). `n_max` is the maximum of the long-term loudness (sone) - the predictor of the loudness of sounds up to about 5 s (clause 7.9) - and `loudness_level_max` the phon value it maps to. `percentiles` gives the long-term-loudness values (sone) exceeded for the stated fraction of the active trace (e.g. `percentiles[5]` is the level exceeded 5 % of the time); the standard itself reports only the peak long-term loudness (clause 9), the percentiles are provided as a convenience. `field` and `presentation` echo the listening conditions. ### MooreGlasbergTimeVaryingLoudness.plot() ```python MooreGlasbergTimeVaryingLoudness.plot( ax: Axes | None = None, *, language: str = 'en', **kwargs: Any, ) -> Axes ``` Plot the short-term and long-term loudness against time. Requires matplotlib (`pip install phonometry[plot]`); returns the `Axes`. See `._plotting`.