Speech Transmission Index (STI)

A public-address system, an intercom, a reverberant lecture hall: each is a transmission channel between a talker’s mouth and a listener’s ear, and each degrades speech in its own way. The Speech Transmission Index (STI) of IEC 60268-16 rates that channel with a single number in $[0,1]$ by measuring how much of the speech envelope survives the trip. This page covers the modulation-transfer physics behind the index, the indirect method from a measured room impulse response, and the direct STIPA measurement with its standardized test signal.

Note

STI vs SII. The STI characterises a transmission channel (how much of the speech modulation a room or sound system preserves) while the SII predicts intelligibility from audibility: how much of the speech spectrum clears the noise and the hearing threshold at the listener’s ear. For the latter, see the Speech Intelligibility Index guide.

How do I compute the IEC 60268-16 Speech Transmission Index in Python?

From a measured room impulse response, call speech.sti_from_impulse_response(ir, fs, snr=25.0). The result gives sti on the 0 to 1 scale, its Annex F rating letter and the seven octave-band modulation transfer indices. For a direct measurement, play speech.stipa_signal(fs) in the room and pass the recording to speech.stipa(recording, fs).

1. The modulation transfer function

Reverberation and noise do not muffle speech uniformly; they blur its envelope: the slow (0.63–12.5 Hz) intensity modulations that carry syllables. STI quantifies how much of that modulation survives from mouth to ear, per octave band, as the modulation transfer function $m(F)$. A delta-like channel keeps $m=1$ (STI = 1); reverberation low-passes the envelope following Schroeder’s closed form, and steady noise scales it:

$$m(F)=\frac{1}{\sqrt{1+{\left(2\pi F\frac{T_{60}}{13.8}\right)}^2}}\cdot \frac{1}{1+10^{-\mathrm{SNR}/10}}$$

Modulation depth is the thing worth measuring because intelligibility rides on the depth of the envelope valleys, not on the loudness of the peaks. A talker alternates energy bursts (vowels) with near-silences (stop gaps, fricative onsets) at syllable rate, and a listener segments speech by hearing those dips. A reverberant tail fills the dips from behind, since late energy smears into the gaps; steady noise raises their floor. In both cases the received modulation depth shrinks, and with it the contrast between speech sounds, even when the average level barely changes. The full method probes $m(F)$ at 14 modulation frequencies (0.63 Hz to 12.5 Hz in one-third-octave steps) in each of the 7 octave bands from 125 Hz to 8 kHz, converts each $m$ to an effective signal-to-noise ratio clipped to ±15 dB, and combines the results, band-weighted, into the index: the STI is an effective SNR of the envelope, mapped onto $[0,1]$.

That is a claim about a waveform, so it is worth watching on one. The clip below sends a fully modulated 4 Hz envelope — one syllable-rate burst and gap per quarter second — through the 1 kHz octave band of a room, and lets first the reverberation time and then the noise take it apart. The received trace is the probe convolved with the very $h_k^2(t)$ the Schroeder integral above runs on, so the depth $(\max -\min )/(\max +\min )$ you can measure off the screen is the $m$ the library returns. Both halves are drawn at a constant received mean, which is the whole point: a sound level meter pointed at any frame of this clip reads the same number, and the index does not.

A fully modulated four-hertz intensity envelope is received through the one-kilohertz octave band of a room. As the reverberation time sweeps from 0.30 to 2.50 seconds the peaks fall and the valleys fill toward a mean line that never moves, and the measured modulation depth falls from 0.90 to 0.26; the modulation transfer curve beside it drops at every one of the fourteen modulation frequencies, the seven band modulation transfer indices fall with it and the speech transmission index walks from 0.84 down to 0.40. Then, at a fixed one-second reverberation time, the speech-to-noise ratio sweeps from 25 to 0 decibels: a shaded noise floor rises under the trace instead, the mean still does not move, and the depth falls again to 0.28 for a speech transmission index of 0.36.

Download the animation (WebM)

Two mechanisms, one quantity. Reverberation smears energy across the gaps, so the envelope shrinks about its mean; steady noise fills the gaps from below, so a floor rises under it. Neither changes the average level, and both destroy the contrast a listener segments syllables by.

A fully modulated four-hertz intensity envelope is received through the one-kilohertz octave band of a room. As the reverberation time sweeps from 0.30 to 2.50 seconds the peaks fall and the valleys fill toward a mean line that never moves, and the measured modulation depth falls from 0.90 to 0.26; the modulation transfer curve beside it drops at every one of the fourteen modulation frequencies, the seven band modulation transfer indices fall with it and the speech transmission index walks from 0.84 down to 0.40. Then, at a fixed one-second reverberation time, the speech-to-noise ratio sweeps from 25 to 0 decibels: a shaded noise floor rises under the trace instead, the mean still does not move, and the depth falls again to 0.28 for a speech transmission index of 0.36.

Download the animation (WebM)

Two mechanisms, one quantity. Reverberation smears energy across the gaps, so the envelope shrinks about its mean; steady noise fills the gaps from below, so a floor rises under it. Neither changes the average level, and both destroy the contrast a listener segments syllables by.

The two degradations look nothing alike on that curve, which is why the index needs the whole $m(F)$ rather than one number:

Reverberation and noise degrade the same quantity in two distinguishable ways. A decay is a low-pass filter on the envelope, and lengthening it moves the corner down (the dashed lines are the closed form above, which the measured points follow closely). Steady noise instead multiplies the whole curve by $1/(1+10^{-\mathrm{SNR}/10})$, which is flat in $F$ — at 0 dB that factor is exactly one half. A single modulation frequency cannot tell the two apart, which is why the full method probes fourteen of them and STIPA still probes two per band (marked on the left panel for this band).

Show the code for this figure

import matplotlib.pyplot as plt
import numpy as np
from phonometry import speech

fs = 48000
rng = np.random.default_rng(0)

# The 14 full-STI modulation frequencies (0.63 Hz to 12.5 Hz, third-octave).
MOD_FREQS = np.array([0.63, 0.80, 1.00, 1.25, 1.60, 2.00, 2.50,
                      3.15, 4.00, 5.00, 6.30, 8.00, 10.0, 12.5])


def decay(t60):
    n = np.arange(int(2.5 * t60 * fs))
    return rng.standard_normal(n.size) * np.exp(-6.9078 * n / fs / t60)


fig, (ax_t, ax_n) = plt.subplots(1, 2, figsize=(12.6, 5.2))
for t60 in (0.3, 0.9, 2.5):
    mtf = speech.sti_from_impulse_response(decay(t60), fs).mtf[3]   # 1 kHz
    ax_t.semilogx(MOD_FREQS, mtf, "o-", label=f"T60 = {t60} s")
    closed = 1 / np.sqrt(1 + (2 * np.pi * MOD_FREQS * t60 / 13.8) ** 2)
    ax_t.semilogx(MOD_FREQS, closed, "--")

ir = decay(0.9)
for snr in (20.0, 10.0, 0.0):
    mtf = speech.sti_from_impulse_response(ir, fs, snr=snr).mtf[3]
    ax_n.semilogx(MOD_FREQS, mtf, "o-", label=f"SNR = {snr:g} dB")
ax_t.legend(); ax_n.legend()
plt.show()

The markers are full pipeline runs of sti_from_impulse_response on synthesized decays; the dashed line is the analytic Schroeder prediction, so their agreement is the indirect method’s own sanity check. The shaded ladder down the right margin is the Annex F qualification scale from U to A+, the rating letter the result returns, so the curve reads directly as the class a room of that reverberation time can reach.

Show the code for this figure

# Both features of the figure: the measured points against the closed form,
# and the Annex F ladder they are read against.
t60_points = [0.3, 0.5, 0.8, 1.2, 2.0, 3.0, 5.0]
alpha = np.array([0.085, 0.127, 0.230, 0.233, 0.309, 0.224, 0.173])  # Ed.5 male
beta = np.array([0.085, 0.078, 0.065, 0.011, 0.047, 0.095])


def analytic_sti(t60):
    m = 1 / np.sqrt(1 + (2 * np.pi * MOD_FREQS * t60 / 13.8) ** 2)
    snr_eff = np.clip(10 * np.log10(m / (1 - m)), -15.0, 15.0)  # the ±15 dB clip
    mti = np.full(7, ((snr_eff + 15.0) / 30.0).mean())
    return float(alpha @ mti - beta @ np.sqrt(mti[:-1] * mti[1:]))


measured = [speech.sti_from_impulse_response(decay(t), fs).sti
            for t in t60_points]
print(np.round(measured, 3))     # [0.827 0.731 0.641 0.555 0.437 0.368 0.269]

fig, ax = plt.subplots(figsize=(10, 6))
edges = [0.36, 0.40, 0.44, 0.48, 0.52, 0.56, 0.60, 0.64, 0.68, 0.72, 0.76]
letters = ["U", "J", "I", "H", "G", "F", "E", "D", "C", "B", "A", "A+"]
for lo, hi, letter in zip([0.15, *edges], [*edges, 0.95], letters):
    ax.axhspan(lo, hi, color=plt.get_cmap("RdYlGn")(letters.index(letter) / 11),
               alpha=0.18, lw=0, zorder=0)
    ax.text(1.005, (lo + hi) / 2, letter, transform=ax.get_yaxis_transform(),
            va="center", fontsize=8)
dense = np.logspace(np.log10(0.25), np.log10(6.0), 200)
ax.semilogx(dense, [analytic_sti(t) for t in dense], "--")
ax.semilogx(t60_points, measured, "o")
ax.set_xlabel("Reverberation time T60 [s]")
ax.set_ylabel("STI")
ax.set_ylim(0.15, 0.95)
plt.show()

2. Indirect and direct (STIPA) measurement

Setting up the measurement

Both routes below start from a signal that travelled a real path, and clause 7 is specific about what produces it. The page covers two physically different measurements — an unamplified talker in a room, and a sound system driven electrically — and the setup differs mainly in where the signal enters.

The source. For an unamplified talker, use an artificial mouth or mouth simulator with head-and-mouth directivity (the standard points at ITU-T P.51), because in a listening space intelligibility depends on the source directivity. In its absence, a small single-source high-quality loudspeaker with a cone diameter not exceeding 100 mm may be used, and it shall be described with the results. Verify that the source’s one-third-octave frequency response is within ±1 dB over the range the chosen signal needs — 88 Hz to 11.3 kHz for a full-STI or impulse-response signal, or octave band by octave band from 125 Hz to 8 kHz for STIPA — measured in a free field, and equalize it if it is not. Then set it on the axis of the microphone at the real talker position and distance, pointing in the normal speaking direction (clause 7.2 a to c).

The level. Match the operational speech level with the Annex J procedure. Where that match is not available, the standard’s fallback is an equivalent level of 60 dB(A) at 1 m in front of the artificial mouth or test loudspeaker. This is not a detail you can guess from the room: a close-talking microphone sees a speech level of about 86 dB(A) to 94 dB(A) at 5 cm to 2 cm, and a gooseneck about 80 dB(A) to 86 dB(A) at 10 cm to 5 cm (clause 7.2 d). For a sound system the signal is injected electrically instead, as close to the normal input as possible so that every equalizer, delay and processor in the chain is included, and adjusted to the level of speech at that point by the same Annex J method (clause 7.4).

The receiver. The measurement device — microphone, artificial ear or head simulator — shall be acoustically calibrated for sensitivity and frequency response, and the measurement made at the listener’s normal location and listening height (about 1.2 m seated, 1.6 m standing). A single microphone shall be omnidirectional and of diffuse-field type; a directional microphone gives results that do not correlate with the STI model and is not advised (clauses 4.1 and 7.3). For headsets, use an in-ear microphone or an artificial ear. Measure the ambient noise at the same point with the source switched off, so it can be entered through ambient= or as snr=.

Before blaming the room, verify the integrity of the test signal with a loop-back measurement — this catches a corrupted or over-compressed file, and the standard advises against digitally compressed formats, noting that schemes of at least 128 kbit/s have been shown to work (clause 7.2 a).

import numpy as np
from phonometry import speech

fs = 48000
# A measured room impulse response (synthesized decay so the example runs)
ir = np.random.default_rng(0).standard_normal(fs) * np.exp(-6.9 * np.arange(fs) / fs / 0.5)

# Indirect method: from a measured room impulse response
res = speech.sti_from_impulse_response(ir, fs, snr=25.0)
print(f"STI = {res.sti:.2f}  ({res.rating})")   # 0.73 (A)

# Direct STIPA measurement: play speech.stipa_signal() in the room, record it
test = speech.stipa_signal(fs, seconds=18.0, level_db=80.0)
recording = test                       # in practice, the microphone signal after playback
res = speech.stipa(recording, fs)
res.plot()   # per-band modulation transfer index (MTI) bars, STI + rating in the title

Where snr= comes from. It is the second input of the indirect method and it carries the whole noise degradation, so its provenance matters as much as the impulse response’s. For each octave band it is the band level of the speech signal at the listener position minus the band level of the ambient noise measured at that same position with the source switched off — which is why the argument accepts a 7-vector: real ambient noise is rarely flat, and ventilation noise concentrated at 125 Hz and 250 Hz costs the low bands far more than a single broadband figure suggests. With snr=None the calculation assumes a noise-free channel, so the result is the room’s own limit and an upper bound on what any listener will experience; quoting it as a measured STI is exactly what makes a hall look acceptable on paper. Measure the ambient spectrum once, measure or estimate the speech spectrum at the position, and pass the band-by-band difference — or, when the absolute levels matter because auditory masking and the reception threshold are in play, pass level= and ambient= instead so the level-dependent stages of the standard are applied rather than a pure ratio.

Whichever route produced it, the result is worth reading band by band before the single number is quoted: the STI is a weighted combination of seven octave-band modulation transfer indices, and a room usually fails in a particular part of the spectrum rather than uniformly.

Show the code for this figure

import numpy as np
import matplotlib.pyplot as plt
from phonometry import speech

# A reverberant hall (T60 = 0.9 s) measured with a 15 dB speech-to-noise
# ratio: a synthesized exponential decay stands in for the measured IR.
fs = 48000
rng = np.random.default_rng(0)
n = np.arange(fs)
ir = rng.standard_normal(fs) * np.exp(-6.9078 * n / fs / 0.9)
res = speech.sti_from_impulse_response(ir, fs, snr=15.0)
print(round(res.sti, 3), res.rating)      # 0.583 E

# One line: the per-band MTI bars with the STI and its rating in the title.
res.plot()
plt.show()

# By hand, mirroring what STIResult.plot() draws:
bands = [125, 250, 500, 1000, 2000, 4000, 8000]
fig, ax = plt.subplots()
ax.bar(np.arange(len(bands)), res.mti)
ax.set_xticks(np.arange(len(bands)))
ax.set_xticklabels([f"{b}" for b in bands])
ax.set_xlabel("Frequency [Hz]")
ax.set_ylabel("Modulation transfer index MTI")
ax.set_ylim(0.0, 1.0)
ax.set_title(f"STI = {res.sti:.2f} (rating {res.rating})")
plt.show()

In this hall the seven indices sit within 0.06 of each other, the signature of a decay that is uniform across the spectrum plus a broadband noise floor. A profile that sags at 125 Hz and 250 Hz instead points at low-frequency reverberation (too little bass absorption), while one that falls only at 4 kHz and 8 kHz usually means the loudspeaker is out of the listener’s direct-sound coverage, since air and directivity strip the top bands first. Those are different remedies, and only the per-band view distinguishes them.

The direct measurement sends the STIPA signal along the full chain drawn below, from the source through the room to the microphone and into the per-band modulation analysis that yields the index.

stipa emits a UserWarning when the recording is shorter than the recommended 15 s (IEC 60268-16 STIPA practice, 15 s to 25 s): below that the slow modulation components are averaged over too few periods and the STI is biased low (an ideal loopback gives STI ≈ 0.956 at 5 s vs ≈ 0.998 at 18 s).

The implementation follows Edition 5 (2020): Edition 4’s normative PDF is the base and every Ed. 5 change is source-attributed in the code; the only numeric delta is the revised male speech spectrum of clause A.6.1. CI checks the standard’s own verification vectors: the six weighting-factor band pairs to ±0.001 STI, the $m$ ↔ STI mapping table, the level-dependent masking control points, and Schroeder-form decays at four $T_{60}$ values.

The analyzer is also verified end to end against the IEC 60268-16 rev 5 verification test bench signals from stipa.info (Embedded Acoustics BV): the direct-method modulation-depth staircase (Annex C.3.2), the indirect-method exponential decays against the closed-form Schroeder MTF (C.3.3), the filter-bank slope test with a +41 dB unmodulated adjacent-octave tone (C.4.2, $m\ge 0.5$), the weighting-factor band pairs (A.2.2) and the filter-bank phase-distortion test with half-octave edge carriers (A.3.1.2, |STI bias| < 0.01 over TI = 0.1–0.9). All five suites pass with the level-dependent features disabled, as the bench prescribes. The 49 certified WAVs stay local (third-party data, not committed); CI re-derives the same signal constructions synthetically in the conformance suite.

Reading the result: the Annex F bands

Every example on this page ends in a letter, and the letter is the part a client reads. Annex F divides the scale into bands with edges at 0.36, 0.40, 0.44 … 0.76, and Annex G Table G.1 gives an example of what each band is used for. STIResult.rating returns the letter; the nominal STI value below is the centre of the band:

Rating	STI range	Nominal	Typical use (Annex G Table G.1)
A+	≥ 0.76	—	Recording studios; excellent but rarely achievable
A	0.72–0.76	0.74	Theatres, speech auditoria, parliaments, courts, assistive hearing systems
B	0.68–0.72	0.70	Theatres, speech auditoria, teleconferencing
C	0.64–0.68	0.66	Complex messages with unfamiliar words
D	0.60–0.64	0.62	Lecture theatres, classrooms, concert halls
E	0.56–0.60	0.58	Concert halls, modern churches; high-quality PA
F	0.52–0.56	0.54	PA in shopping malls and public buildings, VA systems, cathedrals
G	0.48–0.52	0.50	Target value for voice-alarm systems
H	0.44–0.48	0.46	VA and PA in difficult acoustic environments; normal lower limit for VA
I	0.40–0.44	0.42	VA and PA in very difficult spaces
J	0.36–0.40	0.38	Not suitable for PA systems
U	< 0.36	—	Not suitable for PA systems

Three things follow. The familiar “STI ≥ 0.5” requirement of voice-alarm work is band G, whose comment in Table G.1 is literally “target value for VA systems”; Table G.1’s own NOTE 1 adds that its values are minimum targets. The scale is deliberately coarse — one band per 0.04 STI — because that spacing is “based on the typical uncertainty of direct STI measurements”, so a one-band difference is the smallest worth arguing about and quoting three decimals of STI is false precision. And 0.04 is finer than the ≈ 0.03 run-to-run scatter of a single STIPA measurement noted below, so a letter can move between repeats while the STI has not changed in substance. Annexes F and G are informative, and Edition 5’s Scope says outright that the document does not provide criteria for certifying a transmission channel — so write a project requirement as a numeric STI, and use the letter to report it.

From positions to a rating for the space

The STI is a property of one source-to-listener path, so verifying a hall or a voice-alarm installation is a set of measurements, not one. Put the microphone at ear height for the intended posture (about 1.2 m seated, 1.6 m standing), spread the positions over the served area including the acoustically worst corners rather than the convenient ones, and keep at least one position in each loudspeaker coverage zone of a distributed system: clause 7.6.4 asks for “a representative number of locations”.

The reduction rule is the part that is easy to get wrong. Clause 7.6.4 says that taking a simple mean of the results can be misleading, and that a better single figure, one that accounts for the spatial variation, is the mean minus one standard deviation — sometimes called the rating of the space, and the value a given location has about an 84 % probability of reaching if the results are Gaussian. Better still is to plot the whole statistical distribution. The practical consequence for a fiche: when a requirement is set, say whether the boxed number is one position, the worst position or the mean minus one standard deviation, because those are three different verdicts against the same limit — and the 0.04-wide Annex F bands are the natural resolution at which to summarise the spread.

Direct or indirect: choosing between them

Each route has failure modes the standard is explicit about:

• Non-linear or time-variant channels. The indirect method assumes a linear, time-invariant channel: an impulse response cannot represent clipping, compressors, automatic gain control or a vocoder. For a sound system with non-linear processing in the chain, measure directly: the STIPA signal at least travels through the real chain, and the FULL STI signal is the reliable choice where the distortion is severe (IEC 60268-16 clause 6.3 and Table 3).
• Level-dependent effects. The STI is not level-invariant: auditory masking and the reception threshold act on the absolute band levels at the listener. Play the test signal at the system’s operating level (the standard’s Annex J practice sets it 3 dB above the $L_{Aeq}$ of continuous speech at the position) and pass level= and ambient= so the analysis includes them; an impulse response measured loud and rescaled afterwards misses these effects entirely.
• Impulsive and fluctuating background noise. A dropped tool or babble during a direct measurement corrupts the measured modulation depths (clause 7.13). The standard’s remedy is the indirect route: average the impulse response with MLS or sweeps for a noise-free MTF, then add the noise degradation back via snr= or level=/ambient=. A quick sanity check is to run the analyzer with the source off; the residual STI should stay below 0.20.
• Statistical spread. The STIPA signal is pseudo-random noise, so repeated direct measurements scatter by up to about 0.03 STI even in steady conditions (and more in fluctuating noise); repeat and compare rather than trusting a single run, and respect the minimum duration flagged by the UserWarning above.

The level dependence is the one of those four that is easy to dismiss, so it is worth seeing how large it is:

Same room, same impulse response, one number that changes by more than a third of the scale. Below about 55 dB the speech is barely clear of the room’s own noise and the reception threshold of Table A.3 bites; above about 80 dB the auditory masking of Table A.2 lets the loud low bands mask the high ones. In between there is a broad plateau, which is why measuring at the operating level is not fussiness — an impulse response measured loud and rescaled afterwards sits on the flat dashed line and is only valid for a level nobody recorded.

Show the code for this figure

# `ir` and `fs` are the reverberant hall of this section.
ambient = np.array([45.0, 40.0, 35.0, 30.0, 28.0, 25.0, 22.0])   # dB SPL
# The Ed.5 male speech spectrum of clause A.6.1, relative to the 500 Hz band.
shape = np.array([-2.5, 0.5, 0.0, -6.0, -12.0, -18.0, -24.0])
shape_total = 10 * np.log10(np.sum(10 ** (shape / 10)))

totals = np.arange(40.0, 100.5, 2.5)
curve = [speech.sti_from_impulse_response(
    ir, fs, level=shape - shape_total + t, ambient=ambient).sti for t in totals]
print(round(min(curve), 3), round(max(curve), 3))          # 0.246 0.604
print(round(speech.sti_from_impulse_response(ir, fs).sti, 3))   # 0.609

fig, ax = plt.subplots()
ax.plot(totals, curve)
ax.axhline(speech.sti_from_impulse_response(ir, fs).sti, linestyle="--")
ax.set_xlabel("Overall speech level at the listener [dB SPL]")
ax.set_ylabel("STI")
plt.show()

sti_from_impulse_response() / stipa() parameters

Parameter	Type	Units	Range / default	Notes
ir / x	1D array	any / Pa	non-empty	IR (indirect) or STIPA recording (direct)
fs	int	Hz	> 0
snr	float or 7-vector, optional	dB	default None	Adds steady-noise degradation
level	7-vector, optional	dB SPL	default None	Enables auditory masking + reception threshold (Tables A.2/A.3)
ambient	7-vector, optional	dB SPL	needs level	Ambient noise band levels
reference	1D array, optional (stipa)	—	default None	Measured source signal instead of the nominal $m=0.55$

Both return STIResult: sti, mti (7 bands), mtf (7×14 or 7×2), band_levels, rating (Annex F letter A+…U).

IEC 60268-16 report (.report())

STIResult.report(path) renders a one-page PDF fiche laid out like a voice-alarm / public-address intelligibility verification report: a standard-basis line stating the measurement method (the full STI indirect method from an impulse response, or the direct STIPA method on a recorded signal), an optional metadata header block, a per-octave-band modulation transfer index table beside the per-band MTI bars (the result’s own .plot()), the boxed STI = X single number with the Annex F qualification band, an optional verdict row and a footer with the fixed disclaimer. It uses the same ReportMetadata container and rendering engine as the ISO 717 insulation fiche; a supplied requirement is read as the minimum required STI (a higher STI passes). Rendering needs reportlab and, for the figure the fiche embeds, matplotlib (pip install "phonometry[report,plot]"); only engine="reportlab" is supported. Pass language="es" for a Spanish fiche.

from phonometry import ReportMetadata, speech

res = speech.sti_from_impulse_response(ir, fs)
res.report(
    "sti_fiche.pdf",
    metadata=ReportMetadata(
        specimen="Concourse voice-alarm loudspeaker line",
        measurement_standard="IEC 60268-16",
        laboratory="Phonometry Reference Laboratory",
        requirement=0.5,             # minimum required STI (a higher STI passes)
    ),
)

The example fiche is regenerated with make reports and kept rendered in the repository; click the preview to open the PDF.

One-page speech-transmission-index fiche: a metadata header, an octave-band modulation transfer index table, the per-band MTI bars, the boxed STI = 0.64 single-number result with the Annex F qualification band and a PASS verdict against a 0.5 minimum.

Download the report (PDF)

Speech transmission index fiche (STIResult.report), STI with the Annex F band.

What this guide covers

• Covered

  IEC 60268-16:2020 (Edition 5) for the male speech option, the only one Edition 5 keeps: the modulation transfer function and the m to STI mapping (clauses A.5.2 to A.5.6), the full-STI indirect method from a measured impulse response via speech.sti_from_impulse_response(), the direct STIPA method of Annex B via speech.stipa_signal() and speech.stipa(), the Ed.5 male test-signal spectrum of clause A.6.1, the level-dependent auditory masking and reception threshold corrections of Tables A.2 and A.3 (level= and ambient=), and the Annex F rating letters returned as STIResult.rating.

• Not covered

  The direct full-STI measurement (the 14-modulation-frequency test signal played and recorded through the real chain, per clause 6.3 and Table 3, recommended above when distortion is severe) is not implemented: only the STIPA direct signal (stipa_signal/stipa) and the indirect full-STI computation from an impulse response are available. The female speech option is not missing from the library: Edition 5 itself removed it (foreword, item d), so there is nothing left to implement.

See also

• Room Acoustics: the measured impulse response the indirect method consumes, and the open-plan metrics (ISO 3382-3) built on per-position STI.
• Speech Intelligibility Index: the audibility-based ANSI S3.5 index that complements the STI.
• Loudness and Sound Quality Metrics: loudness, sharpness, tonality and roughness of the received sound.
• Theory: the modulation-transfer derivation and the $m$ ↔ STI mapping.
• API reference: speech.sti.
• Theory: Modulation transfer and STI: the modulation transfer function, why m is a ratio of modulation depths, and how the octave-band m matrix collapses to one index.

References

• Houtgast, T., & Steeneken, H. J. M. (1985). A review of the MTF concept in room acoustics and its use for estimating speech intelligibility in auditoria. The Journal of the Acoustical Society of America, 77(3), 1069-1077. https://doi.org/10.1121/1.392224The modulation-transfer framework of section 1 and the m ↔ STI mapping the index is built on.
• International Electrotechnical Commission. (2020). Sound system equipment — Part 16: Objective rating of speech intelligibility by speech transmission index (IEC 60268-16:2020 (Edition 5)). The modulation transfer function and the m ↔ STI mapping, the STIPA test signal and direct method, the indirect method from the impulse response, auditory masking and the reception threshold (Tables A.2/A.3), the revised male speech spectrum (clause A.6.1) and the Annex F rating letters. Edition 4's normative PDF is the base and every Ed. 5 change is source-attributed, the only numeric delta being the revised male speech spectrum of clause A.6.1.