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Audiology Review: Preparing for the Praxis and Comprehensive Examinations
https://t.me/medicina_free
102
test in quiet. Construct validity is what is completed when a test is being
developed/updated. An example of this would be the examination of items on
a survey to explore their relatedness, divided into themes, and using these relationships to explain the variable results from the survey. Content validity is also
important to consider in the development or use of test measures or questionnaires (e.g., if a test is designed to measure a patient’s speech recognition, does
the test item analysis result in an equivalent distribution of phonemes used in
everyday language?). Face validity is like content validity but is a more subjective
approach
An example of predictive validity would be how well pure-tone averages predict
hearing aid satisfaction in patients
validity would be measured between two tests, for instance, the Dichotic Digits
and the Competing Sentences Test, to see how well the patient’s performance
aligns on measures of similar auditory processing skills.
Reliability
— from a quick review of a test, is it testing what it purports to test?
— hint, hint, not very well. Finally, concurrent
n
Test-retest reliability measures the consistency or results when repeating the same test on the
same sample at a different point in time. An example of test-retest reliability assessment is
seen in the common clinical procedure of retesting 1000 Hz at the beginning of pure-tone
threshold testing.
n
Interrater reliability (between-rater reliability) is a measure of consistency used to evaluate the
extent to which different judges agree in their assessment. A common example is determining
the reliability between two “judges” in defining when a behavioral response (e.g., head turn) is
present during visual reinforcement audiometry.
n
Intrarater reliability (within-rater reliability) refers to the consistency of observations or
judgments of one rater over several trials and is best determined when multiple trials are
administered over a short period of time.
Sensitivity and Specificity
n
Sensitivity refers to the relationship between a true positive (i.e., hit) and a false negative
(i.e., miss) when the condition is positive. Sensitivity describes how well a condition is
detected when the condition is present.
n
Specificity refers to the relationship between false positives (i.e., false alarms) and true negatives
(i.e., correct rejections) when the condition is negative. Specificity describes how well a
condition is rejected when the condition is absent.
CHAPTER 3 Acoustics, Psychoacoustics, and Instrumentation
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CASE EXAMPLE
An audiologist working in a hospital-based clinic is tasked with evaluating otoacoustic emission (OAE) equipment for purchase. The audiologist requests data
(shown below) from the manufacturer of two models (Model A and Model B)
and finds the following information for each. In each box are the number of
individuals who meet the response types (e.g., listeners with hearing loss who
refer on the test) and listeners with normal hearing who pass the test. Based on
these data, the audiologist decides to calculate the sensitivity and specificity of
the two units. Both models are identical in cost at $10,000, which includes all
equipment and fees associated with initial training.
103
Hearing Status
Model
(A)
Refer 40
Pass 2
To calculate sensitivity, listeners who have hearing loss and do not pass
the screening (refer) are of concern. In the table for Model A, this is calculated
using the formula a/(a + b) or 40/(40 + 2) = .95 or 95% sensitivity. Specificity
is concerned with those that do not have a condition (normal) and do not test
positive for the condition (pass). In the table for Model A, this is calculated
using the formula d/(c + d) or 1000/(1000 + 100) = 0.91 or 91% specificity.
Calculating the sensitivity and specificity for Model B yields different
results. Sensitivity was calculated at 97% (70/[70 + 2] = 0.97), with specificity
at 98% (1200/[1200 + 20] = 0.98). Comparing these results to Model A, the
sensitivity of both models is similar (95% vs. 97%), but the specificity for Model
B is much higher than Model A (98% vs. 91%). This means that Model B is
more effective at determining those who do not have hearing loss as not having
hearing loss (pass). Given that both pieces of equipment are equivalent in cost
and produce similar sensitivity results, the audiologist selects Model B based on
the specificity data.
Hearing
Loss
(a)
(b)
Normal
100
(c)
1000
(d)
Hearing Status
Model
(B)
Refer 70
Pass 2
Hearing
Loss Normal
(a)
1200
(b)
20
(c)
(d)
Psychoacoustics
Psychoacoustics is the study of the psychological response to acoustic signals. In addition to the
profession of audiology, fields including engineering, cognitive and experimental psychology, and
telecommunications are concerned with psychoacoustic principles and their applications.
Audiology Review: Preparing for the Praxis and Comprehensive Examinations
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Power Spectrum Model and the Critical Band
One of the most important findings influencing the study of hearing was the discovery of the auditory
critical band by Fletcher (1940). In his experiments, Fletcher measured the effects of masking noises
with various spectral bandwidths on the detection of a pure tone. While holding the spectrum level of
the noise constant, Fletcher observed increasing thresholds with increases in masker bandwidth up to
a certain point (a certain masker bandwidth), and beyond which, minimal threshold shift occurred.
This is shown by the dashed line in Figure 3–10 (dashed line represents the bandwidth of the critical
band) where increases in threshold occur (increases in circle height on the y-axis) with increasing noise
bandwidth until the critical band is reached (represented by the dashed line). Please note the minimal
increase in threshold following the critical band (because additional noise masking is outside of the
critical band) demonstrated by thresholds of similar value on the y-axis.
n
Fletcher referred to the bandwidth at which negligible increases in masking occurred as the
critical band and suggested that only masking signals falling within the critical band would
influence perception of the signal tone.
n
Fletcher’s findings are considered instrumental to understanding auditory masking and have
subsequently shaped the understanding of effective masking and frequency selectivity in the
auditory system.
The power spectrum model of masking resulted from Fletcher’s early work and posited
that the auditory system acted as a series of bandpass filters (commonly referred to as
36
34
32
30
28
26
24
22
20
18
Threshold (dB SPL)
16
14
12
10
100 1000
Critical BW
Bandwidth (Hz)
10,000
FIGURE 3–10. Effects of masker bandwidth on audibility (threshold detection). Source: Adapted with permission from Fletcher, H.
(1940). Auditory patterns. Reviews of Modern Physics, 12, 47–66.
Copyright 1940 by the American Physical Society.
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auditory filters) that are tuned to specific frequencies. This model also stated that masking
effectiveness is determined by the power of a masker falling within an individual auditory
filter (or critical band) near the test signal (for steady-state maskers). See the upcoming
Knowledge Checkpoint for a calculation related to masker bandwidth and critical
auditory filter bandwidth — a computational example intended to strengthen the reader’s
understanding of masking effectiveness relative to masker bandwidth.
KNOWLEDGE CHECKPOINT
At its core, masking procedures in clinical audiology are guided by discoveries
that lead to development of the power spectrum model. Specifically, the use of
narrowband maskers for masking pure-tone signals and wideband signals for
masking speech signals (signals that extend to numerous critical bands) coincides
with the power spectrum model and concept of the critical band. The calculation provided here is from an undergraduate hearing science text by Lass and
Donai (2023), which demonstrates the relationship between effective masking
of wideband signals when obtaining pure-tone thresholds. Note that toward the
end of the calculation the critical band value for 1000 Hz is specified at 64 Hz.
This calculation demonstrates that only a portion of the 6000 Hz bandwidth
masker is effective at masking a 1000 Hz tonal signal (because only maskers
falling within the 1000 Hz critical band of 64 Hz provide effective masking).
105
Question: What is the effective masking value of a wideband noise (e.g., bandwidth of 6000 Hz) with an overall sound pressure level of 80 dB SPL for a
1000Hz pure tone?
Answer: First, determine the level per cycle (LPC):
LPC = 80 dB SPL – 10(log
6000)
10
= 80 dB SPL – 10(3.77)
= 80 dB SPL – 37.7 = 42.3 dB SPL
Now apply the formula for effective masking (EM):
42.3 dB SPL + 10(log
10
CB)
The critical band for a 1000 Hz pure tone is 64 Hz:
log10 of 64 = 1.81
Therefore:
EM = 42.3 dB SPL + 10(1.81) = 42.3 + 18.1 = 60.4 dB SPL
This means that in the presence of 80 dB SPL wideband noise, the threshold for a 1000 Hz tone will be elevated to 60.4 dB SPL. This will vary slightly
among individuals because of differing abilities to detect a signal in noise. In
Audiology Review: Preparing for the Praxis and Comprehensive Examinations
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testing hearing, however, hearing level (HL) measures are used. Audiometric
zero at 1000 Hz is approximately 7 dB SPL (for headphones). Therefore, the
threshold for a 1000 Hz tone in the presence of this noise will be approximately
53.4 dB HL (60.4 dB SPL – 7 dB SPL).
Now to demonstrate effective masking using a masker bandwidth that is
identical to the bandwidth of the critical filter, perform the same calculation
using a masker bandwidth of 64 Hz (instead of 6000 Hz) again with an overall
sound pressure level of 80 dB SPL. What is the expected finding? If the critical
band for 1000 Hz is 64 Hz and a masker of 64 Hz centered at 1000 Hz is used,
all 80 dB SPL is effective at masking the pure-tone signal because all of the
frequency components contributing to masking fall within the critical band.
Frequency Selectivity
Fletcher discovered that the auditory system acts as a series of band-pass filters centered at specific
center frequencies. Later, it was discovered that these filters increase in width with increasing frequency.
Patterson (1976) was influential in developing the notched-noise technique (which is a variation on
Fletcher’s early techniques) for measuring auditory filter width.
n
Figure 3–11 provides the authors’ interpretation of the notched-noise method. The left
portion of the figure shows a tonal signal centered at a given frequency and various notch
bandwidths (#1–4). On the right, corresponding thresholds for tone detection for each notch
bandwidth (labeled #1–4) are provided. As shown, when the notch bandwidth is small (more
noise contained within the critical band shown in #1), higher sound levels are required
for threshold detection. As the notch bandwidth increases, lower sound levels are required for
threshold detection (due to less noise within the critical band as shown in #4).
Frequency selectivity refers to the ability of the auditory system to process or resolve individual
frequency components contained within a complex signal. This ability is influenced by the width of
the auditory filter in that portions of the auditory system with narrower filter widths provide for better
frequency selectivity than those with broader filtering. On a linear scale, low-frequency auditory filters
are narrower and exhibit better frequency resolution than broader high-frequency auditory filters.
Auditory filter width is quantified using equivalent rectangular bandwidth (ERB) values, with lower
ERB values representing narrow auditory filter with and higher ERB values representing broader
filter width.
n
Harmonic resolvability refers to auditory system ability to resolve (or individually process in
distinct auditory filters) the harmonic structure of a signal. Harmonic resolvability is often
displayed using an auditory/basilar membrane excitation pattern shown in Figure 3–12.
n
When viewing auditory excitation patterns, harmonics creating distinct peaks are considered
resolved, with those not creating distinct peaks in the pattern being considered unresolved. As
shown in Figure 3–12, lower-frequency harmonics are resolved along the basilar membrane,
with higher-frequency harmonics remaining unresolved in a normal auditory system. This is
why, in part, lower-frequency tonal signals evoke a stronger perception of pitch than highfrequency signals.
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FIGURE 3–11. Illustration of notched-noise method for deriving auditory filter width.
FIGURE 3–12. Excitation pattern demonstrating resolved and unresolved har-
monics for normal hearing and hearing-impaired individuals. Source: Adapted
from Hearing Science Fundamentals, Second Edition (pp. 1–370) by Lass, N.
J., & Donai, J. J. Copyright © 2023 Plural Publishing, Inc. All rights reserved.
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n
Cochlear damage creates broader auditory filtering (and higher ERB values) and reduced
frequency selectivity across the range of frequencies involved, as shown in Figure 3–12
(Impaired Hearing). Reduced frequency selectivity leads to poorer speech recognition abilities
due to decreased processing of the fine acoustic features and differences in the speech signal.
AUDIOLOGY NUGGET
Often in clinical practice, audiologists are faced with the situations where
increased audibility through amplification is needed, but speech recognition
abilities are poor or reduced. This typically presents as patients facing clinical
issues with attenuation (hearing loss on the audiogram) and distortional components of hearing loss (reduced speech recognition abilities). The distortional
component is caused by several factors, but one underlying reason is reduced
frequency selectivity, which cannot be improved with amplification (as with
audibility or the attenuation component). In these instances, it is important to
effectively counsel patients to the fact that HAs will provide needed audibility
while providing realistic expectations regarding understanding speech.
Effects of Sound Duration on Audibility
The human auditory system integrates sound energy over time (approximately every 10 ms), which is
often referred to as a sliding temporal integration window. Auditory sensitivity (threshold detection)
improves as the duration of a stimulus is lengthened from the shortest duration that produces a perception of tonality up to a certain point.
n
As shown in Figure 3–13, once a tonal signal reaches a duration of approximately 300 to
400ms, negligible improvements in signal detection threshold are observed. Significant
reductions in the sound level required for audibility occur for changes in very brief signals
(e.g., a signal increasing from 5 to 50 ms sees a substantial improvement in audibility for the
50-ms signal compared to the 5-ms signal).
n
This change in sensitivity with duration is referred to as temporal integration (summation).
The temporal integration function for persons with normal auditory function appears to be
constant over a wide range of frequencies.
n
This concept is important in terms of behavioral and electrophysiological threshold
determination.
Effects of Sound Duration on Spectral Content
Similar to the effects on amplitude, signal duration impacts the spectral content of a signal. It is
well established that the shorter the duration of a signal, the broader the resulting spectral content.
For example, a click stimulus is usually a very brief signal (0.1 ms) with a rapid onset and offset
time, yielding a rectangular pulse and spectral pattern similar to that of white noise. This is used in
electrophysiology (specifically, ABR) for its stimulation of a large area of the cochlea and its advantage
in producing higher neural synchrony than other stimuli. Another stimulus used in ABR is the tone
pip, which is still a brief signal (often < 10 ms), but with a tighter, frequency-specific center. Through
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FIGURE 3–13. Effect of signal duration on audibility. Source: Adapted
from Hearing Science Fundamentals, Second Edition (pp. 1–370) by
Lass, N. J., & Donai, J. J. Copyright © 2023 Plural Publishing, Inc. All
rights reserved.
windowing (e.g., Blackman window), spectral splatter (acoustic energy falling outside of the center
frequency) can be reduced, but still, the duration of a pip yields a less “pure” tone than the tonal stimuli
used in behavioral audiometry. As a side note, the variability in the duration and windowing of ABR
stimuli is why they oftentimes cannot be universally calibrated to ANSI standards.
KNOWLEDGE CHECKPOINT
In behavioral testing, stimuli are presented with a relatively long rise and fall
time to ensure frequency-specific signals are presented to the listener. However,
in electrophysiological measures, such as the ABR, a longer rise/fall time significantly degrades the quality of the recorded response. Thus, the trade-off
is between producing a frequency-specific signal (which is more place specific
along the basilar membrane) and obtaining a usable recorded ABR waveform.
With rapid onsets and offsets, the frequency spectrum is broad, as shown at
the top of Figure 3–14 (1-ms rise and fall time), but through ramping, or more
slowly turning the signal onto full amplitude and slowly turning the signal off,
the frequency spectrum becomes tighter, or more “pure,” as shown in the 4-ms
rise and fall time example (Figure 3–14). Windowing a signal with a Blackman
window, for example, produces similar effects to increasing the rise and fall time
of a signal, which is why this type of window is commonly used in ABR testing.
Audiology Review: Preparing for the Praxis and Comprehensive Examinations
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FIGURE 3–14. Effect of rise-fall time on the spectrum
of a 2000 Hz tone burst. Source: Adapted from Auditory
Brainstem Evoked Potentials: Clinical and Research
Applications (pp. 1–379) by Krishnan, A. Copyright ©
2023 Plural Publishing, Inc. All rights reserved.
Masking: Effects of Masker Location, Type,
and Amplitude Modulation
Masking is defined as the process of one signal obscuring the perception of another signal. Numerous
signals are used as maskers by the profession of audiology. Some examples include narrowband noise,
wideband noise (e.g., white or pink noise), speech-shaped noise, International Collegium of Rehabilitative Audiology (ICRA) noise, and multitalker babble.
n
Masking is generally reduced to two types: energetic and informational.
Energetic masking: any kind of acoustic energy that is obscuring a target auditory signal,
such as white noise; occurs in the peripheral auditory system
Informational masking: acoustic energy (i.e., energetic masking) with meaningful content,
such as speech; occurs in both the peripheral and central auditory systems
n
Spatial release from masking (SRM) refers to the phenomenon where changes in masker
location relative to the location of a signal of interest (often speech) “release” or enhance
the perception of the signal. For example, if a speech signal and masker are co-located at 0°
azimuth, it will be more difficult to recognize the signal than if the masker was moved to 90°
while maintaining the position of speech at 0°. This advantage occurs due to an increase in
spatial and interaural cues. For additional details, please review Litovsky (2012).
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n
Modulation masking release (MMR) and temporal glimpsing/dip listening occur when a
masker fluctuates in amplitude over time (a modulated masker) and creates transient breaks in
the noise from which information (tones or speech) can be quickly extracted. Early research by
Hall et al. (1984) found improvements in detection of a pure tone in modulated background
masker compared to a steady-state (unmodulated) masker.
AUDIOLOGY NUGGET
Extracting information during temporal dips in background noise has clinical
implications for the use of dynamic amplitude compression parameters in hearing
aids (release time in particular). A trade-off exists between signal distortion and
increasing the speed at which gain is restored following compression by decreasing the release time of a compressions system. In other words, as the release time
is decreased (making the release time shorter and increasing gain to precompression levels more quickly), there is an increase in distortion of the overall shape
of the signal (temporal envelope) that may negatively impact signal intelligibility. This quick increase in gain may, however, lead to an improved chance of
temporal glimpsing/dip listening due to an increase in audibility (because of
quickly increasing gain) during the reduced amplitude of background noise. For
a thorough review of compression parameters, the trade-offs that exist related
to compression speed, and their effects on the recognition of auditory signals,
please review Moore (2008).
111
Binaural Hearing
It is well established that for most individuals, listening with two ears is preferable to one. Binaural
hearing allows for improved localization of stimuli, better (lower) thresholds (also known as binaural
summation), better hearing in noise, and better frequency selectivity. A summary of the benefits to
binaural hearing is shown in Table 3–1. Binaural hearing can also be referred to as diotic listening (same
signal delivered to both ears at the same time) or dichotic listening (different signals delivered to both
ears at the same time).
Localization
Binaural signals are first processed at the level of the SOC in the auditory brainstem, as this is the
first place that receives input from both peripheral auditory pathways. Vertical localization relies on
spectral changes in acoustic signals, as a function of head, torso, and pinna, as signal sources change
in elevation. The pinna is primarily effective at altering the frequency spectra above 3 kHz, where the
head and torso are impactful below 3 kHz. Horizontal localization primarily relies on a combination
of interaural intensity (level) differences (IID/ILD) and interaural timing (phase) differences (ITD/
IPD) between the ears.
n
High-frequency information (>1500 Hz) is responsible for IID/ILDs, mostly due to the head
shadow effect. When a signal is coming from precisely in front of a listener (0° azimuth),
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