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phonometry
Esta documentación describe la versión 4.0.0, todavía sin publicar. La versión actual en PyPI es la 3.3.0 y no incluye todo lo que se describe aquí.

speech.objective_intelligibility

La referencia de la API se publica en inglés en los dos idiomas: se genera a partir de los docstrings del código, que son su texto original.

Short-Time Objective Intelligibility (STOI and ESTOI).

Implements the two correlation-based objective intelligibility measures that predict the intelligibility of time-frequency weighted noisy speech, where the speech-transmission index (phonometry.speech.sti) and the speech intelligibility index (phonometry.speech.sii) are less appropriate:

  • STOI (Taal, Hendriks, Heusdens and Jensen 2011, An Algorithm for Intelligibility Prediction of Time-Frequency Weighted Noisy Speech, IEEE TASLP 19(7); short version Taal et al. 2010, ICASSP): the average, over short-time segments and one-third-octave bands, of the correlation between the clean and degraded short-time temporal envelopes, after a per-segment normalisation and a signal-to-distortion clipping.
  • ESTOI (Jensen and Taal 2016, An Algorithm for Predicting the Intelligibility of Speech Masked by Modulated Noise Maskers, IEEE/ACM TASLP 24(11)): the extended measure that mean- and variance-normalises the short-time band-by-frame spectrogram over both rows (band envelopes) and columns (spectra), so the intermediate index is a spectral correlation. It tracks intelligibility better under modulated maskers and competing talkers, where STOI’s band-independent correlation overestimates.

Both measures share the same front end: resampling to 10 kHz, a 256-sample (25.6 ms) Hann-windowed, 50 %-overlapping short-time Fourier transform zero-padded to 512 points, removal of the clean-speech frames more than 40 dB below the loudest clean frame, a 15-band one-third-octave grouping of the DFT magnitudes from a lowest centre of 150 Hz, and 30-frame (384 ms) analysis segments. The output is a scalar in roughly [0, 1] with a monotonic relation to the fraction of correctly understood words: 1 for a degraded signal identical to the clean one, and near 0 for uncorrelated noise.

The band grouping snaps each one-third-octave edge to the nearest DFT bin and the analysis window is MATLAB’s hanning (numpy.hanning of length N+2 with its zero end-points dropped), matching the reference MATLAB implementation of the authors; pystoi reproduces the same conventions and is used as an external cross-check in the test suite (it is not a runtime dependency).

Auto-generated from the source docstrings by scripts/generate_api_docs.py (make api-docs). Do not edit by hand.

stoi

Section titled “stoi”
stoi(
    clean: Signal | list[float] | np.ndarray,
    degraded: Signal | list[float] | np.ndarray,
    fs: int | None = None,
    *,
    extended: bool = False,
) -> STOIResult

Short-time objective intelligibility of degraded speech.

Computes STOI (Taal et al. 2011) or, with extended=True, ESTOI (Jensen & Taal 2016): a scalar with a monotonic relation to the intelligibility of degraded relative to the clean reference clean. Both signals are resampled internally to 10 kHz, split into one-third-octave short-time envelopes, and compared over 384 ms segments.

Parameters

NameDescription
cleanThe clean reference speech (1-D). Accepts a phonometry.io.Signal; STOI normalises every band of every segment before correlating them, so a calibration factor on either record cancels and the score does not move. The factor is applied anyway, so that the samples entering the computation are the same ones every other surface on this contract would see.
degradedThe degraded/processed speech (1-D, same length as clean, same sample rate).
fsSample rate of both signals, in hertz. Required when both records are bare arrays; either may be a Signal and supply it, and two Signals recorded at different rates are refused rather than arbitrated.
extendedUse ESTOI (spectral correlation, robust to modulated maskers) instead of STOI.

Returns: A STOIResult with the index .value and its .plot().

Raises

ExceptionWhen
ValueErrorif the signals are not equal-length finite 1-D arrays, fs is not positive, or fewer than 30 short-time frames survive the silent-frame removal (too little speech to score).

STOIResult

Section titled “STOIResult”
STOIResult(
    value: float,
    extended: bool,
    segment_scores: NDArray[np.float64],
    band_scores: NDArray[np.float64] | None,
    band_frequencies: NDArray[np.float64],
    sample_rate: int,
)

Result of a STOI or ESTOI intelligibility computation.

Attributes

NameDescription
valueThe overall intelligibility index (a scalar with a monotonic relation to the fraction of correctly understood words; 1 when the degraded signal equals the clean one).
extendedTrue for ESTOI (Jensen & Taal 2016), False for STOI (Taal et al. 2011).
segment_scoresPer-segment intermediate intelligibility (averaged over bands for STOI, the spectral correlation d_m for ESTOI).
band_scoresPer-band mean intermediate correlation over the segments (STOI only; None for ESTOI, whose index mixes the bands).
band_frequenciesThe 15 one-third-octave band centre frequencies, in hertz.
sample_rateThe internal sample rate the measure runs at (10 kHz).

STOIResult.plot()

Section titled “STOIResult.plot()”
STOIResult.plot(
    ax: Axes | None = None,
    *,
    language: str = 'en',
    **kwargs: Any,
) -> Axes

Plot the intermediate intelligibility that averages to the index.

For STOI this is the mean correlation per one-third-octave band; for ESTOI, whose index mixes the bands, it is the spectral correlation per analysis segment. Requires matplotlib (pip install phonometry[plot]); returns the Axes.

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