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Auditory masking patterns in bottlenose dolphins (Tursiops truncatus) with natural, anthropogenic, and synthesized noise

  • Naval Facilites Engineering Command Pacific
  • Scripps Institution of Oceanography (SIO)

Abstract and Figures

Auditory masking occurs when one sound (usually called noise) interferes with the detection, discrimination, or recognition of another sound (usually called the signal). This interference can lead to detriments in a listener's ability to communicate, forage, and navigate. Most studies of auditory masking in marine mammals have been limited to detection thresholds of pure tones in Gaussian noise. Environmental noise marine mammals encounter is often more complex. In the current study, detection thresholds were estimated for bottlenose dolphins with a 10 kHz signal masked by natural, anthropogenic, and synthesized noise. Using a band-widening paradigm, detection thresholds exhibited a pattern where signal thresholds increased proportionally to bandwidth for narrow band noise. However, when noise bandwidth was greater than a critical band, masking patterns diverged. Subsequent experiments demonstrated that the auditory mechanisms responsible for the divergent masking patterns were related to across-channel comparison and within-valley listening.
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Auditory masking patterns in bottlenose dolphins (Tursiops
truncatus) with natural, anthropogenic, and synthesized noise
Brian K. Branstetter
National Marine Mammal Foundation, 2240 Shelter Island Drive, No. 200, San Diego, California 92106
Jennifer S. Trickey, Kimberly Bakhtiari, Amy Black, and Hitomi Aihara
G2 Software Systems Inc., 4250 Pacific Highway, Suite 125, San Diego, California 92110
James J. Finneran
U.S. Navy Marine Mammal Program, Space and Naval Warfare Systems Center Pacific, Code 71510, 53560
Hull Street, San Diego, California 92152
(Received 8 November 2012; revised 8 January 2013; accepted 16 January 2013)
Auditory masking occurs when one sound (usually called noise) interferes with the detection, dis-
crimination, or recognition of another sound (usually called the signal). This interference can lead
to detriments in a listener’s ability to communicate, forage, and navigate. Most studies of auditory
masking in marine mammals have been limited to detection thresholds of pure tones in Gaussian
noise. Environmental noise marine mammals encounter is often more complex. In the current study,
detection thresholds were estimated for bottlenose dolphins with a 10 kHz signal masked by natural,
anthropogenic, and synthesized noise. Using a band-widening paradigm, detection thresholds
exhibited a pattern where signal thresholds increased proportionally to bandwidth for narrow band
noise. However, when noise bandwidth was greater than a critical band, masking patterns diverged.
Subsequent experiments demonstrated that the auditory mechanisms responsible for the divergent
masking patterns were related to across-channel comparison and within-valley listening.
C2013 Acoustical Society of America. []
PACS number(s): 43.80.Lb, 43.66.Dc, 43.66.Gf, 43.66.Nm [WWA] Pages: 1811–1818
Contrary to the title of Jacques-Yves Cousteau’s 1956
documentary “The Silent World,” we know the oceans are not
now and never have been silent. Long before the industrial rev-
olution gave rise to increasing levels of anthropogenic noise
(McDonald et al., 2006), sounds from weather, seismic, and
biological sources filled ocean environments. Aquatic animals
had the relative luxury of gradually evolving coping mecha-
nisms in response to these noisy environments. However, the
rapid and recent onset of anthropogenic noise has been abrupt
on an evolutionary time scale. Auditory adaptations suited for
an ancestral acoustic environment may be ill-equipped to cope
with today’s rising ocean noise. Consequently, marine mam-
mals may experience behavioral alterations, auditory masking,
physiological damage, and potentially death.
Auditory masking occurs when one sound interferes
with a listener’s ability to hear another sound. Auditory
masking can interfere with an animal’s ability to effectively
forage, detect predators, navigate, and communicate. Studies
of masking in marine mammals have, for the most part, been
limited to simple stimuli such as Gaussian (G) noise and
pure tone signals. This approach has been useful for mapping
parameters related to auditory peripheral filters [e.g., critical
bandwidths (Johnson, 1968;Au and Moore, 1990;Southall
et al., 2003)] and auditory filter shapes (Finneran et al.,
2002), as well as for extrapolating the effects of masking
noise in the marine environment (Clark et al., 2009). A find-
ing consistent across mammalian species, including marine
mammals, is that the auditory periphery behaves as a series
of continuously overlapping bandpass filters (Greenwood,
1990). When detecting a tonal signal in noise, the noise
within a band centered on the signal has the most masking
effect (Fletcher, 1940). In theory, an energy detector at the
output of an auditory filter registers an increase in energy
when a signal is present (Branstetter et al., 2007). If the
increase in energy in that filter is sufficient, a decision is
made that a signal is present. These findings represent a spe-
cial case of auditory masking in which the instantaneous am-
plitude of the noise is sampled from a G distribution.
Recent marine mammal masking studies have demon-
strated that auditory masking is not limited to noise within a
single auditory filter if the noise is non-G (Branstetter and
Finneran, 2008;Erbe, 2008;Trickey et al., 2011). These
findings are consistent with previous studies with humans
(Hall et al., 1984) and a few other animal listeners (Klump
et al., 2001;Pressnitzer et al., 2001). For comodulated (CM)
noise, in which amplitude fluctuations are correlated across
auditory filters, a release from masking occurs, a result
known as comodulation masking release (CMR) (Hall et al.,
1984). The magnitude of CMR can be defined as the differ-
ence in thresholds estimated in G and CM noise (Schoone-
veldt and Moore, 1989). Natural ocean-borne noise [e.g.,
snapping shrimp (SS)] can also be CM, leading to a release
from masking (Trickey et al., 2011). Before understanding
can be gained about the auditory mechanisms that govern
masking in complex noise, basic masking studies using
Author to whom correspondence should be addressed. Electronic mail:
J. Acoust. Soc. Am. 133 (3), March 2013 V
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realistic noise types are warranted. The goals of this study
were to (1) measure auditory masking patterns for a broad
range of noise types and (2) if divergent masking patterns
exist (threshold differences as a function of noise type), con-
duct successive experiments to determine the auditory mech-
anisms responsible.
A. Participants
Two female Atlantic bottlenose dolphins (Tursiops trun-
catus) participated in the study: SAY (age 36) and APR (age
27). The dolphins had normal hearing at the frequencies tested
as determined via auditory evoked potential testing (Houser
and Finneran, 2006). One of the dolphins (SAY) had prior ex-
perience with psychoacoustic tasks while APR was new to
psychophysical testing. They were housed in 9 m 9m or
9m18 m floating netted enclosures (pens) located in San
Diego Bay, California. The study followed a protocol
approved by the Institutional Animal Care and Use Commit-
tee of the Biosciences Division, Space and Naval Warfare
Systems Center Pacific, and all applicable U.S. Department of
Defense guidelines for the care of laboratory animals.
B. General procedure
Data were collected using the same procedures and ap-
paratus as Trickey et al. (2011). A brief description of the
procedures is presented here.
1. Signal presentation
Experimental sessions were conducted in a 3 m 9m
testing pen, and the average ambient noise spectral density
level was generally 75 dB re: 1 lPa
/Hz at 10 kHz. The dol-
phin was required to station on an underwater bite plate and
then phonate in response to a tone (tone trial) or remain silent
if no tone was present (catch trial). During each dive, the dol-
phin completed between 2 and 15 trials, as determined by a
random schedule. A one-down, one-up adaptive staircase pro-
cedure was used to adjust the level of the signal and to esti-
mate thresholds at the 50% correct detection level (Levitt,
1971). The initial step size of the signal was 5 dB, followed
by a 2 dB step size after the first miss/reversal. The order of
tone trials and catch trials was pseudo-randomized (Geller-
man, 1933), where 50% of the trials were “signal present”
and the other 50% were “signal absent” catch trials. A session
continued until at least 11 reversals were reached. The first re-
versal (associated with the 5 dB step size) was always
excluded when determining the threshold, which was calcu-
lated by averaging the last ten reversals. Thresholds where the
false alarm rate exceeded 20% were rejected from analysis.
A. Stimuli and procedure
Signals and noise were calibrated and generated using
the same procedure as in Trickey et al. (2011). The signal
was a 500 ms, 10 kHz pure tone with 50 ms onset–offset lin-
ear ramps to reduce spectral splatter. Bandpass noise was cen-
tered on the signal frequency and was continuously projected
from the same transducer as the signal. Noise was saved as
.wav files, and was looped continuously during each experi-
mental session. The bandwidth of the noise was an independ-
ent variable and ranged between 100 and 8000 Hz. The noise
was bandpass filtered (32nd order Butterworth filter, Cool
Edit Pro 2.0) to produce a flat pressure spectral density level
of 95 dB re: 1 lPa
/Hz. Three noise categories were used for
this experiment: Natural, anthropogenic, and synthesized
noise. Synthesized noise types included G noise and CM
noise, and were the same noise files as in Branstetter and Fin-
neran (2008). Natural and anthropogenic noise were obtained
via field recordings and included natural biological sounds
(SS, humpback whale song, pinniped chorus, whale social
sounds), natural non-biological sounds [rain (RN), ice squeaks
(IS)], and anthropogenic sounds [pile saw (PS), motor boat
(BT) noise, C-tractor tug boat sound, vibratory hammer, seis-
mic airgun, vessel echo-sounder]. Of these sounds, the hump-
back whale song, pinniped chorus, whale social sounds, and
C-tractor sounds were rejected because most of the spectral
energy was below the signal frequency of 10 kHz. Transient
sounds (seismic airgun, vessel echo sounder, and vibratory
hammer) were also rejected because their short durations
would only mask a small temporal portion of the 500 ms sig-
nal. The remaining noise types used in the experiment are pre-
sented in Fig. 1and Table I. See the Appendix for details of
how each noise type was acquired or generated. All noise
types were filtered (FFT filter, Cool Edit Pro 2.0, Syntrillium
Software Corp., Phoenix, AZ) to compensate for the transmis-
sion voltage response of the Reson ITC 1001 transducer
(Slangerup, Denmark). The dolphin SAY participated in this
B. Results
Masking patterns for the different noise types are found
in Fig. 2. Assuming the dolphin’s auditory filter is approxi-
mately 1 kHz wide, masking patterns for within-channel noise
are similar, suggesting that the auditory system processes
these sound types in a similar manner. However, masking pat-
terns diverge for bandwidths greater than 1 kHz, suggesting
that different auditory mechanisms operate on different noise
types. Diverging masking patterns can also be seen in Fig. 3,
in which the standard deviation of the data from Fig. 2is plot-
ted as a function of bandwidth. Again, a sharp increase in the
standard deviation for noise bands greater than an auditory fil-
ter bandwidth suggests that different mechanisms govern
masking patterns for wideband noise.
Critical ratios (CRs) derived from wideband G noise
maskers are often used to predict masking with real environ-
mental noise. To determine how well G noise CRs predict
the current masking results, CRs were calculated for the
thresholds masked in the 8 kHz noise bands for each noise
type. In dB, the CR can be defined as
CR¼Sth N;
where S
is the level of the signal at threshold (re: 1 lPa),
and Nis the noise pressure spectral density (re: 1 lPa
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A 22 dB range resulted, with G noise having the median CR
(Fig. 4). Tukey’s honestly significant difference was used to
compare thresholds in G noise to thresholds in the other
noise types resulting in a significant difference for CM
(P¼0.001), SS (P¼0.009), and IS (P¼0.003) noise. Mask-
ing by RN, PS, and BT noise was not significantly different
than G noise masking.
According to the power spectrum model of masking
(PSM), only noise energy within an auditory filter centered
on a signal contributes to the masking of that signal (Moore,
1993). However, the masking release (MR) observed with
CM and SS noise, the masking increase observed with IS
noise, as well as CMR from previous studies (Branstetter
and Finneran, 2008;Trickey et al., 2011) demonstrate that
noise beyond an auditory filter can have an effect on the dol-
phin’s ability to detect tonal signals. One proposed mecha-
nism to explain CMR is that the auditory system extracts the
temporal envelope from a bank of auditory filters and com-
pares AM patterns across auditory filters (Hall and Grose,
1988;Branstetter and Finneran, 2008). If a sinusoidal signal
is added, the filter centered on the signal will be decorrelated
relative to the flanking bands, providing a temporal cue that
a signal is present. Hypothetically, this cue will not work for
G noise because across-channel envelopes are already
TABLE I. Noise types used in Experiment I. Each noise type was saved as a
Microsoft.wav file with a specified duration and sampling rate. For source
information, please see the Appendix.
Noise type Duration Sampling rate Source
BT 60 s 44.1 kHz 1
PS 30 s 96.0 kHz 2
IS 5 s 100.0 kHz 3
RN 15 s 40.0 kHz 4
SS 3 min 96.0 kHz 1
G 3 min 44.1 kHz 5
CM 3 min 44.1 kHz 5
FIG. 2. Detection thresholds as a function of masker bandwidth for each
noise type. (G ¼Gaussian, CM ¼comodulated, PS ¼pile saw, BT ¼boat
noise, RN ¼rain, IS ¼ice squeaks, SS ¼snapping shrimp). Each data point
represents the average of four thresholds. Error bars were removed for
clarity but the standard deviation was less than 3 dB for each data point.
FIG. 3. Signal threshold standard deviations as a function of masker band-
width. Each data point represents the standard deviation of thresholds for all
noise types combined. Standard deviations are low and consistent up to
1 kHz, at which point the standard deviation increases. The 1 kHz breakpoint
represents the limit of the auditory filter resulting in processing transition.
FIG. 1. (Color online) Spectrograms of noise types used in Experiment I.
All noise types displayed are 8 kHz wide (6 to 14kHz). Gaussian (G), como-
dulated (CM), snapping shrimp (SS), rain (RN), boat noise (BT), pile saw
(PS), ice squeaks (IS). All noise types had flat averaged spectral densities of
95 dB re: 1 lPa
/Hz (40 averages, 500 ms samples).
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decorrelated. Branstetter and Finneran (2008) demonstrated
that when amplitude modulated noise was decorrelated
across channels, CMR was disrupted. How this mechanism
works has not been investigated in dolphins. In Experiment
II, stimuli were created to systematically decorrelate across-
channel envelopes by delaying the temporal envelope of a
band of noise centered on the signal (signal band) compared
to two flanking bands.
A. Stimuli and procedure
Noise samples used in Experiment I (G and CM, 8 kHz
bandwidth) were bandpass filtered (12th order Butterworth
filter, MATLAB R2007a) into three bands: A signal band (9.5
to 10.5 kHz) and two flanking bands (6 to 9 kHz and 11 to
14 kHz). The signal band was chosen to be 1 kHz wide,
reflecting the processing transition seen in Figs. 2and 3,
which is assumed to represent the width of the auditory filter
centered at 10 kHz. The signal band relative to the flanking
bands was delayed in time, with the delays ranging between
0 and 1000 ms (Fig. 5). Delays were restricted by the recip-
rocal of the sampling rate (44.1 kS/s). Therefore, actual
delays were integer multiples of 22.67 ls. The individual
noise bands were then digitally added together and saved
as.wav files. The 10 kHz signal and all threshold estimation
procedures were identical to Experiment 1. Dolphin SAY
participated in this experiment.
B. Results
As predicted, the phase delay of the signal band had no
effect on thresholds for G noise (Fig. 6). However, the phase
delay did have the effect of disrupting CMR for CM noise.
Thresholds were relatively flat for delays less than 2 ms and
then increased from 2 to 10 ms at a rate of 0.85 dB/ms (Fig.
7). After 10 ms, the disruption to CMR was complete.
This experiment was repeated with a second dolphin
(APR). However, only two conditions were chosen (0 and
1000 ms delays) to serve as a parsimonious test to determine
whether across-channel coherence was a mechanism that
influenced masked thresholds in a greater variety of noise
types. In addition to G noise, which served as a baseline con-
dition, the three noise types (from Experiment I) that
produce significantly different masked thresholds were used
(CM noise, SS, and IS). Thresholds for each noise condition
are plotted in Fig. 8. Each bar represents the average of four
thresholds and error bars represent standard deviations.
FIG. 4. CRs for the seven noise types. (CM ¼comodulated, SS ¼snapping
shrimp, RN ¼rain, G ¼Gaussian, PS ¼pile saw, BT ¼boat noise,
IS ¼ice squeaks).
FIG. 5. (Color online) Spectrograms of masking noise used in Experiment
II. Maskers were composed of a signal band (9.5 to 10.5 kHz) and two flank-
ing bands (6 to 9 kHz and 11 to 14 kHz). (A) Masking noise with no delay
(correlated) to the signal band and (B) the same masking noise with the sig-
nal band delayed (decorrelated) by 10 ms.
FIG. 6. Thresholds as a function of the phase delay of the signal band in G
noise and CM. Each data point represents the average of four thresholds and
error bars represent standard deviations. The phase delay had no effect on
Gaussian noise. Thresholds for CM noise increased with phase delays
between 2 and 10 ms.
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There was a significant difference between the 0 ms delay
and 1000 ms delay for both CM and SS noise. Delaying the
signal band did not affect thresholds for G or IS noise.
In Fig. 6, the difference in thresholds between G and
CM noise with no delay was greater than 17 dB. This differ-
ence can be expressed as
CMR ¼thGthCM;
where CMR is comodulation masking release and th
are masked thresholds for G and CM noise, respec-
tively. By decorelating the envelopes across channels, a dis-
ruption in the CMR effect was demonstrated but the average
effect between the two dolphins was only 6.8 dB. This sug-
gests that at least one mechanism in addition to across-
channel comparisons is responsible for the remaining release
from masking. One hypothesis is that the listener could
exploit “quiet” temporal loci within the amplitude modulated
noise in order to listen for the presence of the tonal signal.
This strategy is often referred to as “valley” listening or
“dip” listening (Buus, 1985). To test this hypothesis, the
depth of AM of CM noise was adjusted between 0% and
100% modulation depth. At greater modulation depths, the
valleys are relatively “deep,” which should facilitate a
within-valley listening strategy. However, when the depth of
modulation decreases, the CM noise will begin to approxi-
mate G noise and thresholds should increase. To prevent the
dolphins from using the across-channel comparison mecha-
nism from Experiment II, the noise was bandpass filtered to
a bandwidth of 1 kHz. This presumably is the approximate
bandwidth of the auditory filter centered at 10 kHz, and in
theory restricts any masking processes to an auditory filter
centered on the signal.
A. Stimuli and procedure
CM noise was created in which the depth of AM was an
independent variable. To accomplish this, CM noise was first
synthesized by multiplying G noise by low-pass filtered
noise (5th order Butterworth low-pass filter with a cutoff fre-
quency of 100 Hz; MATLAB 2007, Mathworks, Natick, MA).
Next, the Hilbert envelope was extracted from the CM noise
envðtÞ¼ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
where h(t) is the Hilbert transform of the CM noise wave-
form f(t). The depth of AM was scaled and then multiplied
by G noise by
where ais AM depth and n(t) was G noise, and CM
is the
resulting CM noise with a specified AM depth. Different
noise files were created with different AM depths between 0
and 1. Each file had only one modulation depth. CM
then bandpass filtered (12th order Butterworth bandpass fil-
ter with start and stop frequencies of 9.5 and 10.5 kHz,
respectively). This bandwidth was chosen to represent the
approximate width of a dolphin critical band centered at
10 kHz (Branstetter and Finneran, 2008). All noise files had
a sampling rate of 44.1 kS/s and were 3 min in duration. The
signal for this study was the same 10 kHz tone used in the
previous experiments. Calibrations and threshold estimation
procedures were identical to Experiment I. Dolphins APR
and SAY participated in this experiment.
B. Results
Threshold patterns for CM noise with AM depths rang-
ing between 0 and 0.9 were relatively flat (Fig. 9). However,
AM depths above 0.9 resulted in a threshold decrease of
5.5 and 7.3 dB for dolphins APR and SAY, respectively.
The average release from masking for both dolphins was
6.4 dB. Although the masking patterns for both dolphins are
similar, thresholds for SAY are on average 5.8 dB lower than
FIG. 7. Data from Fig. 5with a linear fit to the increasing segment of the
threshold function. Thresholds increased at a rate of 0.85 dB/ms.
FIG. 8. Signal thresholds for the phase delay experiment with different noise
types. The noise types were G, CM, SS, and IS, and the numbers that follow
the abbreviations (0 and 1) represent either 0 s delay or 1 s delay of the sig-
nal band relative to the flanking band. For example, CM1 represents como-
dulated noise with a 1 s delay to the signal band relative to the flanking
band. The p-values are shown for CM noise and SS noise in which the phase
delay resulted in significant differences in thresholds.
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A. Masking patterns
The PSM predicts that thresholds increase as a function
of noise bandwidth but only up to a “critical-bandwidth” that
reflects the limits of a hypothetical auditory filter centered on
the signal. Spectral components of noise that fall outside of
the auditory filter should have no effect on signal detectabil-
ity. In Fig. 2, making patterns for RN, G, PS, and BT noise
types are consistent with the PSM. For these noise types, there
was a general threshold increase up to a 1 kHz bandwidth, and
then threshold patterns were generally asymptotic. The
remaining noise types also display the general trend of a
threshold increase up to 1 kHz. However, beyond 1 kHz, CM
and SS thresholds decrease while IS thresholds continue to
increase. When noise exceeds the width of an auditory filter,
additional mechanisms are required to describe the resulting
masking patterns for CM, SS, and IS noise types. Like human
listeners, the ability of the dolphin’s auditory system to com-
pare or integrate time-domain information across widely
spaced auditory filters appears to be one of the mechanisms
that leads to CMR. The masking pattern for IS noise is unlike
the other noise types in that thresholds displayed a monotonic
increase as a function of bandwidth well beyond a critical
band. Future experiments are needed to determine the mecha-
nism behind this masking pattern.
When noise is AM and coherent across frequency chan-
nels, CMR occurs (Hall et al., 1984). The specific conditions
that govern CMR in dolphins are as follows.
1. CMR increases as a function of bandwidth
(Experiment I)
When CM noise bandwidth exceeds a critical band,
thresholds decrease. More total noise power results in less
masking. This result may appear counterintuitive. However,
if the signal and noise are examined in the time domain in
addition to the spectral domain, the mechanism is less
elusive. A simple model of the mammalian auditory periph-
ery system consists of a bank of auditory filters (Finneran
et al., 2002) followed by half-wave rectification and low-
pass filtering (Viemeister, 1979;Berg, 1996;Branstetter
et al., 2007). This model is often referred to as a leaky inte-
grator or envelope detector (Viemeister, 1979). For CM
noise wider than a single auditory channel, the output of the
model will be multiple correlated envelopes. Adding a sinu-
soid to the signal channel will result in a decorrelated enve-
lope which may cue the presence of a signal. This
comparison can only be made if spectral components of
noise fall into two or more auditory filters and if the noise is
coherently modulated across frequency regions. As the band-
width of CM noise increases, more comparisons can be
made between the signal channel and additional flanking
channels. Presumably, more comparisons result in increased
confirmation that a signal is present, which then leads to
lower thresholds. In the case of G noise, which lacks any co-
herence across frequency regions, this mechanism cannot be
employed. As a result, signal thresholds masked by G noise
asymptote when noise bandwidths exceed a critical band.
2. Temporal resolution affects CMR
Temporal envelope comparisons appear to form the ba-
sis for CMR. Furthermore, the temporal resolution of the
dolphin’s auditory system governs the fidelity of the physio-
logical representation of the temporal envelope. Although
hair cells on the basilar membrane are relatively fast, 8th
nerve fibers are sluggish in comparison (Rhode and Smith,
1986). The result is that high frequency AM rates may not
be faithfully represented at higher levels of the auditory sys-
tem. Results from Branstetter and Finneran (2008) demon-
strate that CMR is greatest for CM noise with lower AM
rates, and this is likely due to the auditory system’s inability
to resolve high frequency AM rates. The temporal modula-
tion transfer function (TMTF) in human listeners has been
shown to be related to CMR (Berg, 1996). No psychophysi-
cal equivalent of a TMTF has been measured for dolphins,
although modulation rate transfer functions using evoked
potentials display a similar low-pass feature.
In Experiment II, delaying the signal band for G noise
had no effect (Fig. 6) because the bands lack any across-
channel correlation at any delay time. For CM noise, phase
differences less than 2 ms had little effect on thresholds,
which may be due to an unresolvable time difference. How-
ever, a disruption to CMR occurs for phase delays greater
than 2 ms and the effect is complete for delays greater than
10 ms (Figs. 6and 7). The neural circuitry for this effect
might resemble a coincidence detector (Jeffress, 1948)
where outputs from each auditory filter (envelope detector)
converge on a common target. When inputs are in phase,
summation will occur and activate the target. Target activa-
tion codes that the envelopes are coherent and are likely
from the same sound source. When the envelopes are out of
phase, summation will not occur. Experiments from the field
of auditory scene analysis demonstrate that the human audi-
tory system segregates and groups sounds based on similar
acoustic features into sound events that can be related to
FIG. 9. Thresholds as a function of modulation depth. The masking pattern
for both dolphins display fairly flat thresholds up to a modulation depth of
0.9, at which point thresholds decrease with increased modulation depth.
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individual environmental sources (Bregman, 1990). One
acoustic feature used for grouping is the AM rate (Bregman,
1990; p. 261). In natural auditory scenes, frequency regions
that have a common AM pattern are likely generated from
the same source, and are thus grouped together (Hall et al.,
1984). Results from Experiment II are consistent with this
interpretation and a coincidence detector may be the neural
mechanism for grouping. The addition of a sinusoidal tone
to the signal band decorrelates the signal band from the
flanking bands, thus inhibiting neural summation. Even a rel-
atively low amplitude sinusoid could have this effect, which
may explain why thresholds in CM noise are relatively low.
Although such a mechanism is speculative, similar circuitry
has been found within animal auditory systems (Carr and
Konishi, 1988).
C. Within-channel masking
Results from Experiment III demonstrate that a release
from masking can occur in narrow band noise that is ampli-
tude modulated. Some authors have argued that this release
from masking is not true CMR because no across-channel
comparison is made (Schooneveldt and Moore, 1989). The
dip-listening mechanism, in which the auditory system
exploits temporal valleys where the signal-to-noise ratio is
small, is consistent with results from Experiment III. Large
modulation depths result in deep valleys and thus lower
thresholds. Wider valleys, which would result from lower
AM rates, should also result in lower thresholds.
Total MR in AM noise may be the cumulative effect of
across-channel and within-channel mechanisms (Table II).
Although the numbers do not add up for each individual dol-
phin, the average across-channel difference (6.8 dB) and
within-channel difference (6.4 dB) for both dolphins, when
added together, are less than 1 dB different from the average
total MR.
D. Implications and future directions
Predictions of auditory masking based on the PSM or
metrics related to the noise spectrum (e.g., CRs, spectral
density, 1/3 octave noise measurements) may result in a
large amount of error. Different noise types in the current
study result in threshold differences within a 22 dB range
(Fig. 4). A more comprehensive model of auditory masking
that incorporates both spectral-domain and time-domain
metrics is needed. Detection of an auditory event is a prereq-
uisite for more complex auditory processing such as discrim-
ination and recognition abilities. Dolphins are hypothesized
to recognize stereotyped whistles and associate them with
specific group members (Janik et al., 2006). Whistle con-
tours and harmonic structures may also aid in maintaining
group cohesion (Lammers and Au, 2003). To accomplish
these tasks, whistle detection is not enough. The animal
must recognize the sound and associate it with something
meaningful if the sound is going to provide any advantage to
the individual. How noise affects recognition of sounds has
not been studied in odontocetes. Finally, the experiments
presented here represent a worst case auditory masking sce-
nario. The signal and noise were assumed to propagate from
the same location. In real auditory scenes, sound sources are
often separated in space, which leads to a spatial release
from masking (Holt and Schusterman, 2007).
We would like to thank the staff and interns of the U.S.
Navy Marine Mammal Program for their support. We also
thank Marc O. Lammers and Jennifer Miksis-Olds for gener-
ously providing us with field recordings of natural noise. Finan-
cial support was provided by the Office of Naval Research.
1. Source notes
1. A recording was made in San Diego Bay with a Reson
TC4032 transducer coupled to a Reson VP 1000 pream-
plifier and a high pass filter of 100 Hz. Recordings were
digitized and saved as .wav files on a M-audio Microtrack
II. Boat noise was produced by dual outboard Evenrude
175 motors at half throttle. The hydrophone was posi-
tioned 5 m from the sound source in front of the boat,
which was moored to a dock.
2. A recording was made in San Diego Bay with a Reson
TC4032 transducer coupled to a Reson VP 1000 preampli-
fier and a bandpass filter of 100 Hz to 100 kHz. The hydro-
phone was approximately 10 m from the sound source.
Recordings were digitized by a National Instruments PCI-
MIO-16 E-1 multifunction board (National Instruments,
Austin, TX) and saved as binary files and later converted
to a .wav file using MATLAB 2007a.
3. A recording was made with a custom hydrophone (re-
ceiver response 160 dB re: 1 mV/Pa) with a flat response
between (100 Hz and 50 kHz). This recording was
acquired by Jennifer Miksis-Olds in the Arctic Ocean off
the coast of Alaska.
4. Recordings were made with an ecological acoustic recorder
(EAR) (Lammers et al., 2008) in 2.5 m depth. Recordings
were made in the Kure Atoll by Marc Lammers.
5. These .wav files were the same files used in Branstetter
and Finneran (2008).
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TABLE II. Mechanisms that contribute to MR. Total MR represents thresh-
old differences between broadband G noise and CM noise (8 kHz band-
width, 95 dB re: 1 lPa
/Hz). Across-channel thresholds represent threshold
differences between 0 and 1000 ms delays from Experiment II, and within-
channel thresholds represent threshold differences between modulation
depths of 0 and 100 from Experiment III. Total MR can be loosely approxi-
mated by combining across-channel and within-channel threshold
Dolphin Total MR Across-channel Within channel
APR 15.0 5.5 5.5
SAY 12.8 8.0 7.3
avg 13.9 6.8 6.4
J. Acoust. Soc. Am., Vol. 133, No. 3, March 2013 Branstetter et al.: Dolphin auditory masking 1817
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1818 J. Acoust. Soc. Am., Vol. 133, No. 3, March 2013 Branstetter et al.: Dolphin auditory masking
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... Many animals have evolved different mechanisms to cope with masking sounds. These mechanisms involve auditory processes, such as dip-listening (Wiley and Richards, 1982;Klump, 1996), co-modulating masking release (Moore, 2003;Branstetter et al., 2013), auditory stream segregation (Bregman, 1990;Vliegen and Oxenham, 1999), spatial masking release (Arbogast et al., 2002;Saberi et al., 1991;Hine et al., 1994;Dent et al., 1997;S€ umer et al., 2009), or use of multi-modal signal integration with other (e.g., visual) cues (Brumm and Slabbekoorn, 2005). Adaptations of acoustic behavior to avoid masking can also be active, for instance, by increasing source levels of sounds (Lane and Tranel, 1971), changing the frequency spectrum (Halfwerk and Slabbekoorn, 2009;Gross et al., 2010;Verzijden et al., 2010), or temporally adapting signals to avoid overlap with noise (Fuller et al., 2007;Planque and Slabbekoorn, 2008). ...
... Evaluation of masking potential in sperm whales in the wild therefore requires extrapolation from what is known from captive studies of other species. The effect of masking on marine mammal hearing has only been studied in a limited number of species in captivity: dolphins, belugas, porpoises, false-killer whales, and killer whales (Au and Moore, 1984;Au, 2014;Kastelein et al., 2005;Erbe, 2008;Bain and Dahlheim, 1994;Branstetter et al., 2013;Popov et al., 2020). ...
... One critical question is whether the detection thresholds adopted in this study, which were measured (Au, 2014) using broadband masking sounds, are representative for tonal masking sounds such as sonar sounds. For longer duration tonal sounds, it is known that various processes, such as comodulated masking release, dip-listening, and harmonic analysis of broadband signals, allow animals to detect signals in natural masked conditions compared to Gaussian noise masking conditions (Erbe, 2008;Branstetter et al., 2008;Trickey et al., 2010;Branstetter et al., 2013;Cunningham et al., 2014). However, to our knowledge, the ability of tonal sounds to mask detection and recognition of short broadband clicks has not been directly investigated. ...
Modern active sonar systems can (almost) continuously transmit and receive sound, which can lead to more masking of important sounds for marine mammals than conventional pulsed sonar systems transmitting at a much lower duty cycle. This study investigated the potential of 1-2 kHz active sonar to mask echolocation-based foraging of sperm whales by modeling their echolocation detection process. Continuous masking for an echolocating sperm whale facing a sonar was predicted for sonar sound pressure levels of 160 dB re 1 μPa2, with intermittent masking at levels of 120 dB re 1 μPa2, but model predictions strongly depended on the animal orientation, harmonic content of the sonar, click source level, and target strength of the prey. The masking model predicted lower masking potential of buzz clicks compared to regular clicks, even though the energy source level is much lower. For buzz clicks, the lower source level is compensated for by the reduced two-way propagation loss to nearby prey during buzzes. These results help to predict what types of behavioral changes could indicate masking in the wild. Several key knowledge gaps related to masking potential of sonar in echolocating odontocetes were identified that require further investigation to assess the significance of masking.
... To test the effects of both amplitude modulation and the degree of coherence across frequency regions, an additional experiment was conducted (Branstetter et al. 2013a). Masking noise was divided into a signal band, which was the width of an auditory filter and centered on a 10 kHz signal, and two flanking bands (see Fig. 8). ...
... However, for CM noise, Branstetter and Finneran 2008) the delay disrupted the CMR effect, resulting in increasing thresholds for delays between 2 and 10 ms (Fig. 9). The increase in thresholds as a function of delay was approximately 0.85 dB/ms delay (Branstetter et al. 2013a). Although CMR was disrupted by decorrelating the envelopes, the effect was only about 7 dB of masking release. ...
... This hypothesis was tested by adjusting the depth of modulation of CM noise from 100 to 0% modulated. When AM depth decreased from 100 to 90%, thresholds increased by 6 dB and then remained stable from 90 to 0% (Branstetter et al. 2013a). These results provided evidence that the total CMR effect was the combination of two factors, across channel coherence (about 7 dB of MR) and dip-listening (about 6 dB of MR). ...
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Anthropogenic noise is an increasing threat to marine mammals that rely on sound for communication, navigation, detecting prey and predators, and finding mates. Auditory masking is one consequence of anthropogenic noise, the study of which is approached from multiple disciplines including field investigations of animal behavior, noise characterization from in-situ recordings, computational modeling of communication space, and hearing experiments conducted in the laboratory. This paper focuses on laboratory hearing experiments applying psychophysical methods, with an emphasis on the mechanisms that govern auditory masking. Topics include tone detection in simple, complex, and natural noise; mechanisms for comodulation masking release and other forms of release from masking; the role of temporal resolution in auditory masking; and energetic vs informational masking.
... We demonstrate that the harbor porpoise benefits from the fluctuations of the level of the background noise and can improve its detection threshold by up to 14.5 dB at low SAM rates. Comodulation masking release (CMR) has been observed in a range of vertebrate species (e.g., Klump and Langemann, 1995, Branstetter and Finneran, 2008, Trickey et al., 2010, Fay 2011, Branstetter et al., 2013, V elez and Bee, 2013 including humans (e.g., Hall et al., 1984, Buus, 1985, Schooneveldt and Moore, 1989, Moore and Schooneveldt, 1990, see also review by Verhey et al., 2003). So far, CMR has been investigated with masker envelope fluctuations being as low as 6.25 Hz. ...
... The result is consistent with the explanation of the effects of masker level and SAM rate that will affect the filling in of the dips in the masker and, therefore, the perceptual representation of modulation depth. The result on the effect of MD in the present study deviates from that observed in two bottlenose dolphins (Tursiops truncatus) that exhibited no MR at a MD of 90% and a 6.4 dB decrease in threshold (and a corresponding increase in MR) only if MD was 100% (Branstetter et al., 2013). ...
Acoustic masking reduces the efficiency of communication, prey detection, and predator avoidance in marine mammals. Most underwater sounds fluctuate in amplitude. The ability of harbor porpoises (Phocoena phocoena) to detect sounds in amplitude-varying masking noise was examined. A psychophysical technique evaluated hearing thresholds of three harbor porpoises for 500–2000 ms tonal sweeps (3.9–4.1 kHz), presented concurrently with sinusoidal amplitude-modulated (SAM) or unmodulated Gaussian noise bands centered at 4 kHz. Masking was assessed in relation to signal duration and masker level, amplitude modulation rate (1, 2, 5, 10, 20, 40, 80, and 90 Hz), modulation depth (50%, 75%, and 100%) and bandwidth (1/3 or 1 octave). Masking release (MR) due to SAM was assessed by comparing thresholds in modulated and unmodulated maskers. Masked thresholds were affected by SAM rate with the lowest thresholds (i.e., largest MR was 14.5 dB) being observed for SAM rates between 1 and 5 Hz at higher masker levels. Increasing the signal duration from 500–2000 ms increased MR by 3.3 dB. Masker bandwidth and depth of modulation had no substantial effect on MR. The results are discussed with respect to MR resulting from envelope variation and the impact of noise in the environment.
... The framework of auditory scene analysis applied to other animals, including macaques, starlings, treefrogs, ferrets, budgerigars, bats, porpoises, and dolphins, indicates that they also organize their acoustic environment into informative auditory streams (Bee 2015;Branstetter and Finneran 2008;Branstetter et al. 2013;Finneran and Branstetter 2013;Fishman et al. 2012;Fishman et al. 2001;Hulse et al. 1997;Klump 2009, 2020;Ma et al. 2010;Moss and Surlykke 2001;Neilans and Dent 2015). Work with bats is of special interest here, because they, like dolphins, are also echolocators and can directly contribute to the auditory scenes they are decoding through their biosonar systems, i.e., how they manage their echoic investigations in part determines the echoes they receive. ...
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Dolphins gain information through echolocation, a publicly accessible sensory system in which dolphins produce clicks and process returning echoes, thereby both investigating and contributing to auditory scenes. How their knowledge of these scenes contributes to their echoic information-seeking is unclear. Here, we investigate their top–down cognitive processes in an echoic matching-to-sample task in which targets and auditory scenes vary in their decipherability and shift from being completely unfamiliar to familiar. A blind-folded adult male dolphin investigated a target sample positioned in front of a hydrophone to allow recording of clicks, a measure of information-seeking and effort; the dolphin received fish for choosing an object identical to the sample from 3 alternatives. We presented 20 three-object sets, unfamiliar in the first five 18-trial sessions with each set. Performance accuracy and click counts varied widely across sets. Click counts of the four lowest-performance-accuracy/low-discriminability sets (X = 41%) and the four highest-performance-accuracy/high-discriminability sets (X = 91%) were similar at the first sessions’ starts and then decreased for both kinds of scenes, although the decrease was substantially greater for low-discriminability sets. In four challenging-but-doable sets, number of clicks remained relatively steady across the 5 sessions. Reduced echoic effort with low-discriminability sets was not due to overall motivation: the differential relationship between click number and object-set discriminability was maintained when difficult and easy trials were interleaved and when objects from originally difficult scenes were grouped with more discriminable objects. These data suggest that dolphins calibrate their echoic information-seeking effort based on their knowledge and expectations of auditory scenes.
... How temperature-induced alterations in soundscape biophony might affect other taxa or the communities which share habitats with snapping shrimp is an open question, but it is well-established that increasing human-produced noise can reduce communication space, mask biologically important signals, increase stress, and cause injuries, auditory and otherwise (e.g., Branstetter et al., 2013;de Soto et al., 2013;Jones et al., 2020;Mooney et al., 2020;Jones 2021). It remains to be explored whether the dominating contribution of snapping shrimp to soundscapes could induce similar deleterious effects when the sound intensity is increased; however, given that their broadband sounds form the background noise floor, patterns of snapping shrimp acoustic activity are relevant to all soundreceptive and sound-producing organisms living in these habitats. ...
Full-text available
The ocean’s soundscape is fundamental to marine ecosystems, not only as a source of sensory information critical to many ecological processes but also as an indicator of biodiversity and habitat health. Yet, little is known about how ecoacoustic activity in marine habitats is altered by environmental changes such as temperature. The sounds produced by dense colonies of snapping shrimp dominate temperate and tropical coastal soundscapes worldwide and are a major driver broadband sound pressure level (SPL) patterns. Field recordings of soundscape patterns from the range limit of a snapping shrimp distribution showed that rates of snap production and associated SPL were closely positively correlated to water temperature. Snap rates changed by 15-60% per °C change in regional temperature, accompanied by fluctuations in SPL between 1-2 dB per °C. To test if this relationship was due to a direct effect of temperature, we measured snap rates in controlled experiments using two snapping shrimp species dominant in the Western Atlantic Ocean and Gulf of Mexico ( Alpheus heterochaelis and A. angulosus ). Snap rates were measured for shrimp held at different temperatures (across 10-30 °C range, with upper limit 2°C above current summer mean temperatures) and under different social groupings. Temperature had a significant effect on shrimp snap rates for all social contexts tested (individuals, pairs, and groups). For individuals and shrimp groups, snap production more than doubled between mid-range (20°C) and high (30°C) temperature treatments. Given that snapping shrimp sounds dominate the soundscapes of diverse habitats, including coral reefs, rocky bottoms, seagrass, and oyster beds, the strong influence of temperature on their activity will potentially alter soundscape patterns broadly. Increases in ambient sound levels driven by elevated water temperatures has ecological implications for signal detection, communication, and navigation in key coastal ecosystems for a wide range of organisms, including humans.
... Clearly, metrics such as a maximum sound pressure level calculated over a long integration window ignore the finer temporal structure of received signals-and thus do not account for overlap in the time domain. However, sound exposure levels (SEL) of signals with different intermittency might not scale linearly with their masking potential (Branstetter et al., 2013;Cunningham et al., 2014;Erbe, 2008)-which is why we opted to consider both combined and separate single-pulse SEL from the CAS and PAS exposures. However, due to presence of clicks on the DTAG and system noise, it was challenging to get reliable measurements of the actual noise distribution the animal was facing. ...
Auditory masking by anthropogenic noise may impact marine mammals relying on sound for important life functions, including echolocation. Animals have evolved antimasking strategies, but they may not be completely effective or cost-free. We formulated seven a priori hypotheses on how odontocete echolocation behavior could indicate masking. We addressed six of them using data from 15 tagged sperm whales subject to experimental exposures of pulsed and continuous active sonar (PAS and CAS). Sea state, received single-pulse sound exposure level (SELsp), whale depth and orientation towards surface, and sonar were considered as candidate covariates representing different masking conditions. Echolocation behavior, including buzz duration and search range, varied strongly with depth. After controlling for depth and angle to the surface, the likelihood of buzzing following a click train decreased with sea state (t = −7.3, p < .001). There was little evidence for changes in 10 tested variables with increasing sonar SELsp, except reduced buzzing consistent with previously reported feeding cessation (t = −2.26, p = .02). A potential Lombard effect was detected during echolocation with sea state and SELsp, despite off-axis measurement and right-hand censoring due to acoustic clipping. The results are not conclusive on masking effects on sperm whale echolocation, highlighting challenges and opportunities for future anthropogenic masking studies.
... Anthropogenic noise not only disturbs behaviors of marine mammals in terms of habitat migration, diving duration, change in swimming speed, and avoidance behavior, but also leads to auditory masking, potential temporary or permanent shifts in hearing thresholds, and even damage to the vestibular, reproductive, and nervous systems (Erbe 2011;Popper & Hawkins 2012;Finneran 2015). Auditory masking can interfere with an animal's ability to forage effectively, detect predators, navigate, and communicate (Branstetter et al. 2013), which is one of the most important direct effects of shipping noise on marine mammals (Erbe et al. 2016). Ship collision with dolphins can be caused by high ship speed or/ and auditory masking leading to difficulties in sensing environments (Hung 2012;Gannier & Marty 2015). ...
Full-text available
The underwater soundscape is an important ecological element affecting numerous aquatic animals, in particular dolphins, which must identify salient cues from ambient ocean noise. In this study, temporal variations in the soundscape of Jiaotou Bay were monitored from February 2016 to January 2017, where a population of Indo‐Pacific humpback dolphins (Sousa chinensis) has recently been a regular sighting. An autonomous acoustic recorder was deployed in shallow waters, and 1/3‐octave band sound pressure levels (SPLs) were calculated with central frequencies ranging from 25 Hz to 40 kHz, then were grouped into 3 subdivided bands via cluster analysis. SPLs at each major band showed significant differences on a diel, fishing‐related period, seasonal, and tidal phase scale. Anthropogenic noise generated by passing ships and underwater explosions were recorded in the study area. The fish and dolphin acoustic activities both exhibited diel and seasonal variations, but no tidal cycle patterns. A negative significant relationship between anthropogenic sound detection rates and dolphin detection rates were observed, and fish detection rates showed no effect on dolphin detection rates, indicating anthropogenic activity avoidance and no forced foraging in dolphins in the study area. The results provide fundamental insight into the acoustic dynamics of an important Indo‐Pacific humpback dolphin habitat within a coastal area affected by a rapid increase in human activity, and demonstrate the need to protect animal habitat from anthropogenic noises.
... Branstetter et al. (2013a) demonstrated that different noise types with identical spectral density levels can result in CRs that vary by as much as 22 dB. Many natural and anthropogenic noise types (e.g., coastal noise from snapping shrimp, Alpheus digitalis) are broadband and amplitude modulated, resulting in comodulation masking release (Branstetter et al., 2013b;Branstetter and Finneran, 2008;Erbe and Farmer, 1998). Furthermore, the similarity between signal type and noise type (e.g., odontocete whistles and ice squeaks) can have a profound effect on levels of masking (Branstetter et al., 2016;Cunningham et al., 2014). ...
Masked detection thresholds were measured for two killer whales (Orcinus orca) using a psychoacoustic, adaptive-staircase procedure. Noise bands were 1-octave wide continuous Gaussian noise. Tonal signals extended between 500 Hz and 80 kHz. Resulting critical ratios increased with the signal frequency from 15 dB at 500 Hz up to 32 dB at 80 kHz. Critical ratios for killer whales were similar to those of other odontocetes despite considerable differences in size, hearing morphology, and hearing sensitivity between species.
... Most research on marine animal audiograms and hearing thresholds is conducted in the absence of background sound or at very low ambient conditions with a higher signal-to-noise ratio than is typical in the marine environment. Only a few studies have addressed signal detection in the presence of noise, which could elucidate detection thresholds of some marine mammal species, e.g., [135][136][137] and review [138]. The authors are not aware of any such studies in species other than marine mammals. ...
Full-text available
The interdisciplinary field of assessing the impacts of sound on marine life has benefited largely from the advancement of underwater acoustics that occurred after World War II. Acoustic parameters widely used in underwater acoustics were redefined to quantify sound levels relevant to animal audiometric variables, both at the source and receiver. The fundamental approach for assessing the impacts of sound uses a source-pathway-receiver model based on the one-way sonar equation, and most numerical sound propagation models can be used to predict received levels at marine animals that are potentially exposed. However, significant information gaps still exist in terms of sound source characterization and propagation that are strongly coupled with the type and layering of the underlying substrate(s). Additional challenges include the lack of easy-to-use propagation models and animal-specific statistical detection models, as well as a lack of adequate training of regulatory entities in underwater acoustics.
Full-text available
This chapter describes the effects of noise on animals in terrestrial and aquatic habitats. Potential adverse effects cover a range of behavioral changes and physiological responses, including—in extreme cases—physical injury and death. The types and severity of effects are related to a number of noise features, including the received noise level and duration of exposure, but also depend upon contextual factors such as proximity, familiarity, and the behavioral state in which animals were exposed. The effects of anthropogenic noise on individual animals can escalate to the population level. Ultimately, species-richness and biodiversity in an ecosystem could be affected. However, our understanding of population-level effects and ecosystem interactions is limited, yet it is an active area of study. Given that noises of human origin can be controlled, there is the potential to mitigate any negative impacts by modifying noise source characteristics or operation schedules, finding alternative means to obtain operational goals of the noise source, or excluding biologically critical habitats or seasons.
"Bregman has written a major book, a unique and important contribution to the rapidly expanding field of complex auditory perception. This is a big, rich, and fulfilling piece of work that deserves the wide audience it is sure to attract." -- Stewart H. Hulse, Science Auditory Scene Analysis addresses the problem of hearing complex auditory environments, using a series of creative analogies to describe the process required of the human auditory system as it analyzes mixtures of sounds to recover descriptions of individual sounds. In a unified and comprehensive way, Bregman establishes a theoretical framework that integrates his findings with an unusually wide range of previous research in psychoacoustics, speech perception, music theory and composition, and computer modeling.
Detectability of a 400‐ms, 1000‐Hz pure‐tone signal was examined in bandlimited noise where different spectral regions were given similar waveform envelope characteristics. As expected, in random noise the threshold increased as the noise bandwidth was increased up to a critical bandwidth, but remained constant for further increases in bandwidth. In the noise with envelope coherence however, threshold d e c r e a s e d when the noise bandwidth was made wider than the critical bandwidth. The improvement in detectability was attributed to a process by which energy outside the critical band is used to help differentiate signal from masking noise, provided that the waveform envelope characteristics of the noise inside and outside the critical band are similar. With flanking coherent noise bands either lower or higher in frequency than a noise band centered on the signal, it was next determined that the frequency relation and remoteness of the coherent noise did not particularly influence the magnitude of the unmasking effect. An interpretation in terms of nonsimultaneous masking was reconciled with some aspects of the data, and with an interpretation in terms of across‐frequency temporal pattern analysis. This paradigm, in which detection is based upon across‐frequency temporal envelope coherence, was termed ‘‘comodulation masking release.’’ Comodulation offers a controlled way to investigate some of the mechanisms which permit signals to be detected at adverse signal‐to‐noise ratios.
Thresholds were measured for a 2000‐Hz signal masked by continuous noise varying in bandwidth from 50 to 3200 Hz in 1‐oct steps. For random noise maskers, thresholds increased with increasing bandwidth up to 400 Hz and then remained approximately constant. When the masker was amplitude modulated by a low‐pass noise, so as to produce coherent envelope fluctuations across frequency, thresholds decreased as the masker bandwidth was increased beyond 200 Hz, giving a CMR. For a 400‐ms signal duration, the CMR for masker bandwidths greater than 400 Hz increased from 2.4 to 12.3 dB as the modulator bandwidth was decreased from 400 to 12.5 Hz in 1‐oct steps. For modulator bandwidths of 50 Hz or less, a release from masking of 3.5 to 7.3 dB occurred even for maskers with bandwidths of 50 and 100 Hz, less than the critical bandwidth at 2000 Hz. For a modulator bandwidth of 12.5 Hz, the CMR decreased from 12.3 to 5.3 dB as the signal duration was decreased from 400 to 25 ms in 1‐oct steps. When the signal duration was less than 100 ms, there was no release from masking for masker bandwidths less than 400 Hz. The results suggest that, for maskers with fluctuating envelopes, across‐channel comparisons can reduce signal thresholds even for short signals, but an extra within‐channel process can produce a release from masking for long signals. This second process may reflect the ability of subjects to detect a change in the statistical properties of the envelope of the stimulus when the signal is added to the masker.
Temporal Modulation Transfer Functions (TMTF&apos;s) were obtained by measuring the threshold amplitude of sinusoidal modulation as a function of modulating frequency. For modulation frequencies below approximately 800 Hz, TMTF&apos;s obtained with a continuous wide‐band noise carrier generally show the low‐pass characteristic reported previously [J. Acoust. Soc. Am. 53, 314(A) (1973)], That is, with increasing modulation frequency the amplitude of modulation required for threshold remains constant up to approximately 10 Hz and then increases monotonically up to 800 Hz. The interpretation is that at high modulation frequencies the auditory system temporally “smooths” the amplitude fluctuations produced by modulation and the observer therefore requires greater modulation amplitude at the input in order to detect the modulation. For modulation frequencies greater than 800 Hz, modulation threshold is independent of modulation frequency and can be predicted from the increment threshold for wide‐band noise. The form of the empirical TMTF generally agrees with that predicted by the familiar model consisting of half‐wave rectification followed by “leaky integration.” The time constant of the integrator is estimated to be 3 msec. [Research supported by NIH.]
Acoustic masking from anthropogenic noise is increasingly being considered as a threat to marine mammals, particularly low-frequency specialists such as baleen whales. Low-frequency ocean noise has increased in recent decades, often in habitats with seasonally resident populations of marine mammals, raising concerns that noise chronically influences life histories of individuals and populations. In contrast to physical harm from intense anthropogenic sources, which can have acute impacts on individuals, masking from chronic noise sources has been difficult to quantify at individ- ual or population levels, and resulting effects have been even more difficult to assess. This paper pre- sents an analytical paradigm to quantify changes in an animal's acoustic communication space as a result of spatial, spectral, and temporal changes in background noise, providing a functional defini- tion of communication masking for free-ranging animals and a metric to quantify the potential for communication masking. We use the sonar equation, a combination of modeling and analytical tech- niques, and measurements from empirical data to calculate time-varying spatial maps of potential communication space for singing fin (Balaenoptera physalus), singing humpback (Megoptera novaeangliae), and calling right (Eubalaena glacialis) whales. These illustrate how the measured loss of communication space as a result of differing levels of noise is converted into a time-varying mea- sure of communication masking. The proposed paradigm and mechanisms for measuring levels of communication masking can be applied to different species, contexts, acoustic habitats and ocean noise scenes to estimate the potential impacts of masking at the individual and population levels.
Presentation series have not previously been rigidly tested for probable chance score, and many have allowed high scores to be made purely through chance. The author furnishes 44 series of 10 presentations of R's and L's, derived from the 1024 possible combinations of the two, by eliminating all that violate any of five described criteria. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
This chapter is concerned with two main areas: frequency analysis and pitch perception. Frequency analysis refers to the action of the ear in resolving (to a limited extent) the sinusoidal components in a complex sound; this ability is also known as frequency selectivity and frequency resolution. It plays a role in many aspects of auditory perception but is most often demonstrated and measured by studying masking. Studies of pitch perception are mainly concerned with the relationships between the physical properties of sounds and the perceived pitches of those sounds and with the underlying mechanisms that explain these relationships. One important aspect of pitch perception is frequency discrimination, which refers to the ability to detect changes in frequency over time and which is (at least partly) a separate ability from frequency selectivity.
Dolphins produce frequency modulated (FM) whistles that are thought to promote the synchrony and coordination of behavior between members of a group. How whistles are used in this regard remains poorly understood. One possibility is that whistles have directionality and thereby convey the orientation and direction of movement of the signaler to nearby listeners. To explore this possibility, whistles from free-ranging Hawaiian spinner dolphins (Stenella longirostris) were obtained using a towed, three-hydrophone line array and examined for the presence of directionality. Both the estimated source level and harmonic content of whistles produced by animals traveling with or toward the array were greater than those of animals moving ahead or away from it. In addition, signals produced by animals near the array (within 20 m) were received differently on the three hydrophones spaced 11.5 m apart. These differences were greater than would be expected from transmission loss disparities alone. The results indicate that directivity is present in the transmission pattern of whistles. To infer the form of this directivity, a theoretical whistle beam pattern was established based on the assumption that the dolphin's sound source is approximated by a circular piston transducer (Au 1993). The resulting beam indicates that spinner dolphin whistles become increasingly directional with frequency, especially with respect to harmonics. The orientation-dependent harmonic structure of whistles thus presents a potential cue that listening animals could interpret to infer the direction of movement of signalers. Harmonics are present in the whistles of many dolphin species and may represent an inherent signal design feature that promotes coordination between animals.