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Accurate species classification of Arctic toothed whale
echolocation clicks using one-third octave ratios
Marie J. Zahn,
1,a)
Michael Ladegaard,
2
Malene Simon,
3
Kathleen M. Stafford,
4
Taiki Sakai,
5
and Kristin L. Laidre
1,6
1
Polar Science Center, Applied Physics Laboratory, University of Washington, Seattle, Washington 98105, USA
2
Zoophysiology, Department of Biology, Aarhus University, Aarhus C 8000, Denmark
3
Greenland Climate Research Centre, Greenland Institute of Natural Resources, 3900 Nuuk, Greenland
4
Marine Mammal Institute, Oregon State University, Newport, Oregon 97365, USA
5
Ocean Associates, Inc., Under contract to Southwest Fisheries Science Center, National Marine Fisheries Service,
National Oceanic and Atmospheric Administration, La Jolla, California 92037, USA
6
Department of Birds and Mammals, Greenland Institute of Natural Resources, 3900 Nuuk, Greenland
ABSTRACT:
Passive acoustic monitoring has been an effective tool to study cetaceans in remote regions of the Arctic. Here, we
advance methods to acoustically identify the only two Arctic toothed whales, the beluga (Delphinapterus leucas)and
narwhal (Monodon monoceros), using echolocation clicks. Long-term acoustic recordings collected from moorings in
Northwest Greenland were analyzed. Beluga and narwhal echolocation signals were distinguishable using spectrograms
where beluga clicks had most energy >30 kHz and narwhal clicks had a sharp lower frequency limit near 20 kHz.
Changes in one-third octave levels (TOL) between two pairs of one-third octave bands were compared from over one mil-
lion click spectra. Narwhal clicks had a steep increase between the 16 and 25kHz TOL bands that was absent in beluga
click spectra. Conversely, beluga clicks had a steep increase between the 25 and 40 kHz TOL bands that was absent in
narwhal click spectra. Random Forest classification models built using the 16 to 25 kHz and 25 to 40 kHz TOL ratios
accurately predicted the species identity of 100% of acoustic events. Our findings support the use of echolocation TOL
ratios in future automated click classifiers for acoustic monitoring of Arctic toothed whales and potentially for other odon-
tocete species. V
C2024 Acoustical Society of America.https://doi.org/10.1121/10.0025460
(Received 30 November 2023; revised 23 February 2024; accepted 9 March 2024; published online 2 April 2024)
[Editor: Brian Branstetter] Pages: 2359–2370
I. INTRODUCTION
In the Arctic, passive acoustic monitoring enables
observations of biotic and abiotic processes over spatiotem-
poral scales that would otherwise be unfeasible (e.g.,
Halliday et al., 2021a;Mattm€
uller et al., 2022;Stafford
et al., 2022). Notably, observations of cetacean occurrence
acquired from passive acoustic data provide key information
about changes in habitat-use and timing of life history
events, or phenology, of Arctic species due to climate
change (Ahonen et al., 2021;Moore et al., 2022;Stafford
et al., 2021). The Arctic has warmed at rates more than three
times the global mean, and sea ice extent at its September
minimum has declined by more than 12% per decade (rela-
tive to 1981–2010 mean; IPCC, 2022;Onarheim et al.,
2018;Rantanen et al., 2022). Greater accessibility in Arctic
waters has spurred increased human activities, such as com-
mercial shipping and oil and gas exploration, that have
heightened underwater noise levels and the risk of acoustic
disturbance (e.g., behavioral changes, masking, or hearing
damage) to marine mammals (Erbe et al., 2016;Finneran
et al., 2002;Halliday et al., 2020;Southall et al., 2021).
Studies using passive acoustics effectively track changes to
natural and anthropogenic sounds, but they require knowl-
edge of unique properties of specific sounds to accurately
identify them in recordings. Given the efficacy and need for
long-term passive acoustic monitoring in the Arctic, a firm
understanding of acoustic parameters must be established
for key species to ensure reliable acoustic classification.
Narwhals (Monodon monoceros) and belugas
(Delphinapterus leucas) are ice-associated Arctic toothed
whales (Monodontidae family). Narwhals inhabit waters in
the Atlantic sector of the Arctic around Greenland, Canada,
Svalbard, and Russia, while the beluga range is circumpolar
and extends into subarctic territories (Hobbs et al., 2019;
Innes et al., 2002;Reeves et al., 2014). Migratory belugas
and narwhals follow the annual sea ice cycle where their
autumn southward and spring northward movements are
linked to sea ice advance and retreat, respectively. They
return to the same summer and winter locations with high
site fidelity, particularly the narwhal (Dietz et al., 2001;
Heide-Jørgensen et al., 2003a;Innes et al., 2002).
Phenological and distribution shifts have been documented
for both species where animals are departing later during
a)
Email: mzahn@uw.edu
J. Acoust. Soc. Am. 155 (4), April 2024 V
C2024 Acoustical Society of America 2359
ARTICLE
...................................
years with delayed sea ice freeze-up and expanding their
range (Hauser et al., 2017;Heide-Jørgensen et al., 2010;
Louis et al., 2020;Nielsen, 2009;Shuert et al., 2022).
Further, narwhals have been shown to be extremely sensi-
tive to ship noise and seismic airgun pulses (Heide-
Jørgensen et al., 2021;Radtke et al., 2023;Tervo et al.,
2023;Williams et al., 2022) and are considered one of the
most sensitive Arctic marine mammals to climate change
due to their preference for cold waters and specialization in
habitat and prey (Heide-Jørgensen et al., 2020;Laidre et al.,
2008). Belugas are also strongly affected by anthropogenic
noise disturbance (Erbe and Farmer, 2000;Halliday et al.,
2021b;Lesage et al., 1999) and some stocks have had sig-
nificant declines from cumulative climate and anthropogenic
stressors (Hobbs et al., 2019;Lesage, 2021).
Narwhals and belugas have been studied using short-
and long-term passive acoustic methods (e.g., Ahonen et al.,
2019;Au et al., 1985;Lammers et al., 2013;Marcoux et al.,
2011;Møhl et al., 1990;Schevill and Lawrence, 1949).
Both species are highly social with complex community
structures, and accordingly, individuals produce a variety of
vocalizations to interact with conspecifics and sense their
environment. Communicative call types produced by these
whales are broadly categorized into whistles, pulsed calls
(or “burst pulses”), and combined calls that contain both
pulsed and tonal sounds (Blackwell et al., 2018).
Vocalizations produced for sensory tasks (e.g., navigation
and prey localization) include echolocation clicks and termi-
nal buzzes that are found at the end of click sequences
(Blackwell et al., 2018;Castellote et al., 2021;Chambault
et al., 2023;Roy et al., 2010). Pulsed calls and buzzes are
defined by their short duration and high-repetition rate sig-
nals. Although there is a considerable body of literature
studying their sounds, beluga and narwhal vocalizations
remain partially described due to recording limitations and
the diversity in their social calls within and between subpo-
pulations (Garland et al., 2015;Marcoux et al., 2012;Sjare
and Smith, 1986). For regions where belugas and narwhals
overlap in West Greenland and the Canadian High Arctic,
distinguishing between their vocalizations is essential for
passive acoustic monitoring of these two species.
Scientific studies have historically estimated key acous-
tic parameters for each species individually (Au et al., 1985,
1987;Koblitz et al., 2016;Miller et al., 1995;Møhl et al.,
1990;Rasmussen et al., 2015;Roy et al., 2010;Stafford
et al., 2012;Zahn et al., 2021a). The first direct species
comparison was done by Frouin-Mouy et al. (2017), where
they examined beluga and narwhal recordings and found
narwhal clicks had a substantial increase in energy between
the 16 and 20 kHz one-third octave bands which was lacking
in beluga clicks. Jones et al. (2022) presented differences in
two key parameters (peak frequency and inter-click inter-
val). It is now well established that beluga and narwhals pro-
duce high-frequency, broadband clicks from 20 kHz to
well over 100 kHz (Koblitz et al., 2016;Rasmussen et al.,
2015;Zahn et al., 2021a), and beluga clicks contain more
energy at higher frequencies (>40 kHz) than narwhal clicks
(Frouin-Mouy et al., 2017;Jones et al., 2022;Zahn et al.,
2021b). Outside of descriptive statistics, Zahn et al. (2021b)
reported the first attempt to automatically classify beluga
and narwhal clicks from parameter estimates using machine
learning Random Forest (RF) models.
Here, we build on previous work and advance methods
to classify beluga and narwhal echolocation clicks using
manual and automated approaches on recordings from pas-
sive acoustic instruments mounted near glacier fronts in
Northwest Greenland. Our primary objective was to investi-
gate the predictive capacity of click parameters derived
from one-third octave levels (TOL) for use in acoustic clas-
sification models. Our findings provide an important step in
automating beluga and narwhal detection in long-term
acoustic recordings using echolocation signals, and thus
improve methods to monitor these species.
II. METHODS
A. Study area
This study was conducted in Northwest Greenland and
was part of a larger project to study the ecological impor-
tance of narwhal summering grounds in Melville Bay
(Fig. 1). Melville Bay (Greenlandic: Qimusseriarsuaq)is
found on the northwest Greenland continental shelf (74N–
76.5N) with a large trough that opens southwest into Baffin
Bay. Both belugas and narwhals utilize regions along the
eastern portion of Baffin Bay throughout the year. The
Eastern High Arctic-Baffin Bay beluga stock occupies estu-
aries, bays, and inlets in the Canadian Arctic Archipelago
during summer months (Hobbs et al., 2019). Over winter, a
portion of the stock resides in the North Water polynya
while the majority travel south during fall along the West
Greenland coast remaining in mostly ice-free waters
(Doidge and Finley, 1993;Heide-Jørgensen et al., 2003b;
Richard et al., 1998b;Richard et al., 1998a). The Melville
Bay narwhal stock spends summer months near glacier
fronts in Melville Bay and migrates south to overwinter in
the dense pack-ice of Baffin Bay and Davis Strait (Dietz
et al., 2008;Dietz and Heide-Jørgensen, 1995;Laidre et al.,
2016;Laidre and Heide-Jørgensen, 2011). Narwhals are
temporary summer residents of Melville Bay. Belugas tran-
sit through the area during their fall and spring migrations.
B. Acoustic data collection
Between 2019 and 2020, three seafloor-mounted ocean
moorings were deployed from the R/V Sanna (Greenland
Institute of Natural Resources, Nuuk, Greenland) near gla-
cier fronts in Melville Bay. Each mooring was equipped
with a sound recorder and an array of oceanographic sensors
to study the habitat of narwhals during summer. Instruments
and floats were attached to a 6 mm Dyneema line, and an
800 kg anchor connected to an acoustic release (PORT LF,
Edgetech Instruments, Hudson, MA) mounted the mooring
to the seafloor. After deployment in August 2019, one moor-
ing was destroyed by icebergs and two were successfully
retrieved in August 2020. These two moorings were
2360 J. Acoust. Soc. Am. 155 (4), April 2024 Zahn et al.
https://doi.org/10.1121/10.0025460
positioned in the Fisher Islands and in front of Kong Oscar
glacier, also known as Nuussuup Sermia (Fig. 1). We refer
to these mooring locations as the Fisher Islands and Kong
Oscar sites.
Moorings included SoundTrap ST500 STD (Ocean
Instruments, New Zealand) acoustic recorders (Table I). The
SoundTrap instruments sampled at 144 kHz (16-bit) with a
Nyquist frequency of 72 kHz to record beluga and narwhal
echolocation clicks that contain most of their energy above
20 kHz (Frouin-Mouy et al., 2017;Jones et al., 2022;
Koblitz et al., 2016;Zahn et al., 2021a). Specifications of
the SoundTrap ST500 STD recorders included a system
peak clipping level of 173 dB re lPa, bandwidth from 20 Hz
to 60 kHz (63 dB), and self-noise <36 dB re 1 lPa above
2 kHz and better than sea-state zero from 100 Hz to 2 kHz.
Acoustic data were logged 40 min per hour. This duty cycle
was selected to ensure the recorders maximized recording
coverage each hour while retaining sufficient battery to last
into winter, well after the area was covered in fast ice and
whales had departed.
C. Acoustic data processing and species assignment
Raw acoustic data were processed using the open-
source passive acoustic monitoring software PAMGuard
(version 2.02.09; Gillespie et al., 2009). Echolocation clicks
were detected using PAMGuard’s Click Detector when the
amplitude of a transient signal exceeded 14 dB above the
measured background noise. Subsets of the SoundTrap data
were processed to adjust filter settings and determine an
effective approach to reduce false detections (i.e., noise)
while maximizing the detection of echolocation clicks. A 4-
pole Butterworth 1 kHz high pass filter (“digital pre filter” in
PAMGuard) was applied to remove a low frequency noise
peak. Then, a 4-pole Butterworth 20 kHz high pass filter
(“digital trigger filter”) was applied for click detection.
Signal waveforms from click detections were labeled and
extracted from the 1 kHz high pass filtered data for subse-
quent analysis.
Following the click detection stage, sound clips were
labeled according to their peak frequency from power
FIG. 1. (Color online) Map of the study region in Melville Bay, Northwest Greenland (a). The dashed outline in (a) demarcates the region (b), and the black
box in (b) shows the region displayed in (c). Ocean mooring locations are marked with yellow diamonds (b), (c). Mooring positions near Kong Oscar glacier
and in the Fisher Islands are shown on a Landsat 8 image converted to natural color from 18 August 2019 (c) courtesy of the U.S. Geological Survey. Ocean
bathymetry (a), (b) is from the International Bathymetric Chart of the Arctic Ocean (Jakobsson et al., 2012).
TABLE I. Summary of acoustic data collected from the moored SoundTrap ST500 STD recorders in Melville Bay.
Site Recording period Latitude (N) Longitude (W) Seafloor depth (m) Sampling rate (kHz) Duty cycle Instrument depth (m)
Fisher Islands 5 Aug 2019–20 Dec 2019 76.1038 61.7270 370 144 40 min/h 194
Kong Oscar 5 Aug 2019– 16 Jan 2020 75.8418 59.8431 250 144 40min/h 158
J. Acoust. Soc. Am. 155 (4), April 2024 Zahn et al. 2361
https://doi.org/10.1121/10.0025460
spectra within PAMGuard. Each classifier, or detector, in
PAMGuard categorized clicks with a unique numeric code
(i.e., detector number) when their peak frequencies fell
within a specified frequency range: 1 (4–20 kHz), 2
(20–50 kHz), and 3 (50–70 kHz). Initial processing of the
data revealed that detector 1 included only false detections
(i.e., non-echolocation sounds), so only detectors 2 and 3
were enabled for downstream analyses. Further inspection
of false detections showed that power spectra from transient
noise signals decreased in energy with increasing frequency.
Therefore, to further reduce abiotic transient sounds (e.g.,
ice or glacier calving), an additional constraint was added to
detector 2, whereby clicks were only labeled as echolocation
clicks when there was a >5 dB difference in energy between
10–20 kHz and 30–40 kHz. This additional constraint was
not applied to detector 3, because there were no significant
abiotic transients detected between 50 and 70 kHz.
Unclassified detections (detector 0) were discarded.
Detections identified and labeled as clicks within
PAMGuard were then processed into hourly acoustic events
using the PAMpal package (version 1.0.4; Sakai, 2023)inR
(version 4.3.1; R Core Team, 2023). Detections were binned
into 1-h long acoustic events with unique identification
numbers. Each event contained all the detections for one or
the other species that occurred within a given hour from the
start to the end date of the recording period for each site
(Table I). Events that had fewer than 30 detections were
found to generally contain only false detections and were
removed.
Concatenated click spectrograms and mean power spec-
tra for all acoustic events were manually examined to iden-
tify each event’s source and classify them as beluga or
narwhal. Events that contained only noise from crackling
sea ice, glacier ice, and calving were removed from subse-
quent classification analyses. Beluga and narwhal events
were identified based on differences in the energy distribu-
tion of each species’ clicks in the frequency domain.
Belugas produce higher frequency clicks compared to those
generated by narwhals as demonstrated by estimated peak
frequencies being consistently >20 kHz higher in beluga
clicks than narwhal clicks (Jones et al., 2022;Koblitz et al.,
2016;Zahn et al., 2021a;Zahn et al., 2021b). This is partly
because narwhal clicks have considerably more energy
between 20 and 30 kHz than beluga clicks (Frouin-Mouy
et al., 2017;Jones et al., 2022). Beluga clicks also do not
contain a low frequency component that is sometimes
observed in narwhal echolocation between 3 and 14 kHz
(Stafford et al., 2012). For events where species designation
was in doubt from concatenated click spectrograms or mean
spectra, raw sound file spectrograms were manually audited
in Raven Pro (version 1.6.5; K. Lisa Yang Center for
Conservation Bioacoustics at the Cornell Lab of
Ornithology, 2023). Additionally, the presence of whistles
aided manual species designation. Marcoux et al. (2012)
describe beluga whistles as having more diversified contours
than those produced by narwhals (e.g., Belikov and
Bel’Kovich, 2007;Sjare and Smith, 1986). The presence of
highly variable whistles below 20 kHz along with high fre-
quency echolocation (click energy mainly >30 kHz) sup-
ported beluga species assignment. Finally, manually
classified events were compared with an independently
labeled dataset at a 6-h resolution produced by a different
human analyst using the original spectrogram data to ensure
no whale detections were missed or incorrectly classified.
D. TOL statistics
Upon completion of the manual species classification for
all acoustic events, we built upon methods developed by
Frouin-Mouy et al. (2017) and calculated the TOL difference
between two sets of one-third octave bands in R (version
4.3.1; R Core Team, 2023). One-third octave band edge fre-
quencies were calculated using the American National
Standards Institute (ANSI) standard base-ten center frequen-
cies also known as decidecade bands (ANSI, 2004). Three
one-third octave bands were selected that best represented dif-
ferences in spectral energy within the recording frequency
bandwidth for the two species: 16 kHz (14 125–17 783 Hz),
25 kHz (22 387–28 184 Hz), and 40 kHz (35 481–44 668 Hz).
We refer to these one-third octave bands by their nominal cen-
ter frequencies throughout in lieu of the actual frequency range
for simplicity. In this study, we considered whether the TOL
in narwhal clicks consistently increased between 16 to 25 kHz
with little difference between 25 to 40 kHz. Conversely, beluga
clicks were expected to have little difference between 16 to
25 kHz and a large TOL difference between 25 to 40 kHz.
For TOL calculations, 1 kHz high pass filtered wave-
forms extracted from PAMGuard were analyzed, and mean
power spectra were produced for each acoustic event (512 pt
fast Fourier transform, FFT). The TOL was calculated by
summing the squared pressures within the upper and lower
one-third octave limits from mean spectra. Then the differ-
ence was computed between the (1) 16 and 25 kHz and (2)
25 and 40 kHz one-third octave bands. The result was two
TOL ratios with potential use in beluga and narwhal click
classification. Hereafter we refer to these two metrics as the
16 to 25 kHz TOL ratio and 25 to 40 kHz TOL ratio.
E. Acoustic classification
Manual species identification of SoundTrap recordings
from the Fisher Islands and Kong Oscar sites yielded two
labeled datasets of hourly acoustic events. We evaluated the
predictive strength of the TOL ratio metrics for species clas-
sification by building RF models using the 16 to 25 kHz and
25 to 40 kHz TOL ratios as predictors. Three RF models
were built using different training datasets that included
TOL ratio estimates from: (1) Fisher Islands and Kong
Oscar (pooled), (2) Fisher Islands, and (3) Kong Oscar.
Additionally, the Fisher Islands and Kong Oscar datasets
functioned as complimentary training and testing datasets.
Thus, site-specific RF models were tested with a dataset dif-
ferent from the one used to train the model. The Fisher
Islands RF model was used to predict Kong Oscar acoustic
2362 J. Acoust. Soc. Am. 155 (4), April 2024 Zahn et al.
https://doi.org/10.1121/10.0025460
events and the Kong Oscar RF model was used to predict
Fisher Islands acoustic events.
RF models have three primary parameters that can be
adjusted: mtry defines the number of randomly selected vari-
ables used to split observations at each node, sampsize indi-
cates the number of observations (i.e., acoustic events) that
are randomly subset to build each tree, and ntree specifies
how many individual trees are built in the forest. Since we
used two variables for classification (16 to 25 kHz and 25 to
40 kHz TOL ratios), only one variable could be randomly
selected so the only possible value for mtry was one. RF
models were configured such that equal subsamples (i.e.,
sampsize) were drawn from each species group without
replacement to account for unbalanced datasets and prevent
overfitting to the majority class. To evaluate model sensitiv-
ity to sampsize, each model was fit over all possible values
of sampsize, and model accuracies were determined.
Possible values of sampsize ranged from 2 to (N1) where
Nis the total sample size of the smallest species class. For
the RF models developed, the accuracies varied by less than
0.01% and therefore were not sensitive to permutations in
sampsize.AsinZahn et al. (2021b), we report model results
when sampsize was equal to half the total sample size for
the smallest (i.e., minority) species class. Models were con-
structed with ten thousand trees (ntree).
RF models use bootstrap sampling to build thousands of
decision trees to produce a final ensemble tree. Random sub-
sets drawn and used as training data are referred to as “in-
bag” samples and the remaining observations are called “out-
of-bag” (OOB) samples. OOB samples are used as testing
data to estimate model prediction errors and accuracy when
RF models are built. Therefore, model performance of
trained RF models was evaluated using OOB correct classifi-
cation scores. Additionally, model stability was confirmed
through plotting the trace of cumulative OOB error rates in
the forest. Consistent performance was observed across ten
thousand trees. The accuracy of trained RF models that were
tested with new data was determined from the percentage of
acoustic events that were correctly classified by the model.
In RF classification models, observations (i.e., acoustic
events) are assigned to a class (i.e., species) based on the
percentage of trees in the forest voting for each species. For
all models, the uncertainty of model predictions was evalu-
ated by examining the distribution of votes for each acoustic
event within a species class. Acoustic events were predicted
with high model certainty when a majority of the trees voted
for the correct species class. All acoustic classification
model development and evaluation of RF models were con-
ducted using the randomForest (version 4.7-1.1; Liaw and
Wiener, 2002) and rfPermute (version 2.5.1; Archer, 2021)
packages in R.
III. RESULTS
A. Manual species identification
SoundTrap instruments recorded for 138 days
(4.5 months; 5 August–20 December 2019) at the Fisher
Islands site and 165 days (5.5 months; 5 August 2019–16
January 2020) at the Kong Oscar site. With a duty cycle of
40 min per hour, recordings totaled approximately 4848 h
(6.7 months) across sites. The PAMGuard Click Detector
identified echolocation clicks including buzzes as well as
non-echolocation high repetition rate calls, referred to in the
literature as burst pulses, or pulsed calls (Fig. 2). The low
frequency component of narwhal clicks was visible between
3 and 5 kHz in some concatenated click spectrograms,
possibly when whales were close to the moorings and the sig-
nal-to-noise ratio was high. During manual inspection of echo-
location events using Raven Pro, lower frequency (<20 kHz)
beluga whistles were observed in some events (Fig. 2).
However, echolocation clicks remained the dominant call type
produced by both species over the recording period.
Manual species identification of hourly acoustic event
data revealed a clear visible difference between beluga and
narwhal echolocation. Beluga clicks contained energy above
30 kHz with peak intensities typically occurring between 40
and 60 kHz (Fig. 3). We note that true spectral peaks occur-
ring in the 60–72kHz range could not be identified due to the
SoundTrap anti-aliasing filter having a 3 dB cut-off fre-
quency at 64.8 kHz. Narwhal clicks consistently presented a
relatively sharp low-frequency cut-off around 20kHz (Fig. 3)
including signals produced during burst pulses and buzzes
(Fig. 2). Although the exact frequency of this lower limit var-
ied between approximately 18 and 23kHz, the presence of a
steep increase in energy around 20 kHz provided an explicit
method to distinguish narwhal clicks from those of beluga. A
full catalogue of labeled beluga and narwhal acoustic event
concatenated click spectrograms and mean spectra showed
clear differences between species (Zahn et al., 2023).
Belugas and narwhals were detected at both the Fisher
Islands and Kong Oscar sites, however, the majority of
detections were, by far, narwhal (92%). No mixed species
events were discovered. From the more than six months of
acoustic data, which was divided into 1-h time bins, a total
of 201 h contained narwhal and beluga clicks. From
PAMGuard’s click detectors 2 and 3 (i.e., clicks with peak
frequencies between 20–50 and 50–70 kHz, respectively),
there were more detections in total, and for each species, at
the Fisher Islands site (117 events total; 776 679 detections)
than the Kong Oscar site (84 events total; 241 920 detec-
tions). At the Fisher Islands, 12 events (141 098 detections)
were beluga and 105 were narwhal (635 581 detections). At
the Kong Oscar site, four events were beluga (1653 detec-
tions) and 80 events were narwhal (240 267 detections).
Narwhals were present in Melville Bay from the start of the
recording period in early August until mid-November when
fall sea ice formation began (Fig. 4). Belugas were detected
only during the month of October (Fig. 4).
B. TOL ratios
Consistent trends between selected TOL were observed
between beluga and narwhal acoustic events. Beluga echolo-
cation power spectra had little change in TOL between the
J. Acoust. Soc. Am. 155 (4), April 2024 Zahn et al. 2363
https://doi.org/10.1121/10.0025460
FIG. 2. (Color online) Selected spectrograms showing beluga (a), (b) and narwhal (c), (d) echolocation clicks with lighter and darker colors indicating higher
and lower sound levels, respectively. All samples were recorded at the Fisher Islands site. Panels (a) and (b) also include beluga whistles in the 5–10 kHz
range and (b) shows a few beluga pulsed calls. Panels (c) and (d) show narwhal burst pulses and echolocation click trains. Spectrograms were produced
using a Hanning window, 50% overlap, and 512 point FFT for (a), (c) and 1024 point FFT for (b), (d) in Raven Pro 1.6.5. Note: the duration of the spectro-
gram (in seconds) differs between subplots with one short (3 s) and one long (20 s) duration example for each species.
FIG. 3. (Color online) Concatenated click spectrograms (a), (c) and mean power spectra (b), (d) for example beluga (a), (b) and narwhal (c), (d) acoustic
events. The number of clicks included in each concatenated click spectrogram is provided on the x-axis (a), (c). Spectrograms and spectra were produced
using a 512 point FFT and Hanning window from 1 kHz high pass waveforms.
2364 J. Acoust. Soc. Am. 155 (4), April 2024 Zahn et al.
https://doi.org/10.1121/10.0025460
16 and 25 kHz bands in contrast to narwhal power spectra
that showed a large increase in magnitude between these
bands (Fig. 5). Conversely, a large difference was seen
between the 25 and 40 kHz TOL in beluga spectra that was
absent in narwhal spectra (Fig. 5). The mean 16 to 25 kHz
TOL ratio was 2.69 dB (median: 2.91 dB) in beluga spectra
and 7.07 dB (median: 7.03 dB) in narwhal spectra. The
mean 25 to 40 kHz TOL ratio was 8.74 dB (median:
10.3 dB) in beluga spectra and 1.35 dB (median: 0.62 dB)
in narwhal spectra. All beluga events had a decrease in TOL
between the 16 and 25kHz bands, corresponding to the
decreasing noise level with increasing frequency. The differ-
ence between TOL ratios (i.e., 16 to 25 kHz TOL ratio minus
the 25 to 40 kHz TOL ratio) was positive for beluga spectra
and negative for narwhal spectra (Fig. 5).
C. Acoustic classification
The three RF models trained with different datasets (1,
both sites; 2, the Fisher Islands site; and 3, the Kong Oscar
site) all had high OOB correct classification rates (>99%;
Table II). All acoustic events were correctly assigned in all
models except for one narwhal event misclassification from
the Fisher Islands. For all RF models developed, variable
importance scores indicated the 16 to 25 kHz TOL ratio was
the more important variable for correct species classifica-
tion. However, exploratory runs of RF models with only the
16 to 25 kHz TOL ratio variable resulted in decreased accu-
racy, and thus the 25 to 40 kHz TOL ratio variable was also
important for correct predictions. Visualizing the distribu-
tion of RF model votes confirmed high confidence in OOB
predictions for all RF models developed (see supplementary
material, Fig. S1).
Site-specific RF models built with TOL ratio variables
predicted the species identity of acoustic events with
extremely high accuracy (100%; Table III). The Fisher
Islands RF model correctly predicted all of the acoustic
events from the Kong Oscar dataset. Similarly, the Kong
Oscar RF model correctly predicted all of the acoustic events
from the Fisher Islands dataset. Visualizing the distribution
of RF model votes for each model confirmed high confidence
in predictions (see supplementary material, Fig. S2).
IV. DISCUSSION
Our results provide compelling evidence for identifying
beluga and narwhal clicks with high certainty. We demon-
strated the predictive strength of two new acoustic classifi-
cation parameters for echolocation signals derived from
one-third octave frequency bands. The differences in TOL
between the 16 to 25 and 25 to 40 kHz one-third octave
bands proved to be robust metrics for automated and manual
species identification. Our TOL classification results suggest
modest sampling rates (96 kHz) are needed to identify
beluga and narwhal clicks which may enable longer duty
cycling or extended deployments in remote areas of the
Arctic. This study builds on existing literature (e.g., Frouin-
Mouy et al., 2017;Jones et al., 2022) and together indicate
that among the diverse vocalizations produced by Arctic
toothed whales, echolocation clicks are dependable signals
for beluga and narwhal acoustic classification.
A. Manual species identification
We show beluga and narwhal clicks can reliably be
identified through visual inspection of spectrograms. Across
all acoustic events examined here, narwhal clicks contained
a distinct spectral peak just above 20 kHz where spectral
energy sharply decreased below 20 kHz and remained rela-
tively flat between 20 and 60 kHz. The frequency peak near
20 kHz is consistent with existing literature documenting
narwhal spectra (Frouin-Mouy et al., 2017;Jones et al.,
2022;Koblitz et al., 2016;Møhl et al., 1990;Stafford et al.,
2012;Zahn et al., 2021b). Pulsed calls and buzzes produced
by narwhals and detected by the PAMGuard Click Detector
FIG. 4. (Color online) Time series of
narwhal (a), (c) and beluga (b), (d)
presence and percent sea ice cover
(blue lines) at the Fisher Islands
(a), (b) and Kong Oscar (c), (d) moor-
ing sites from May 2019 to March
2020. Black bars provide the number
of hours per week that narwhals and
belugas were detected and thus indi-
cate when whales were present. Gray
shaded regions denote when no acous-
tic data were available. Hourly sea ice
concentration data on the secondary
(right side) y-axis were sourced
from the ERA5 reanalysis product
(Hersbach et al., 2023) and were aver-
aged to a daily resolution.
J. Acoust. Soc. Am. 155 (4), April 2024 Zahn et al. 2365
https://doi.org/10.1121/10.0025460
also contained increased energy near 20 kHz. Only in lower
ambient noise conditions were the low frequency compo-
nents of narwhal clicks between 3 and 5 kHz observed. In
contrast to narwhal clicks, beluga click spectra mostly had
energy above 30 kHz. Unlike the sharp 20 kHz lower limit
seen in narwhal spectra, spectral energy in beluga clicks
increased more gradually from 30 kHz and peaked between
40 and 50 kHz, which is potentially a consequence of a
72 kHz Nyquist frequency and an anti-aliasing filter attenu-
ating energy above 60 kHz. Beluga pulsed calls that were
detected in our analyses contained energy from 20 kHz and
above, but they did not present a clear 20 kHz edge like
those produced by narwhals.
These results corroborate findings by Jones et al. (2022)
where beluga and narwhal clicks from moored High-
frequency Acoustic Recording Packages (HARPs) with a
200 kHz sampling rate were analyzed. Jones et al. (2022)
found beluga clicks had higher peak frequencies
(55–60 kHz) than narwhal clicks (23 kHz) at lower received
levels (<130 dB peak-peak). At high received levels
(>150 dB peak-peak), both species had peak frequencies
between 50 and 60 kHz. Broadly, Jones et al. (2022) sum-
marize that narwhal clicks had more energy than beluga
clicks below 40 kHz. Yet, spectral peaks vary depending on
the orientation of the whale relative to the receiver and the
specifications of the recorder. For example, peak frequen-
cies of on-axis clicks were higher for both species, estimated
to be 71 615 kHz for narwhals (Koblitz et al., 2016) and
97 67 kHz for belugas (Zahn et al., 2021a). Nonetheless,
existing literature supports the assertion that belugas pro-
duce clicks with greater energy at higher frequencies com-
pared to narwhals.
B. Acoustic classifier performance
RF models had strong classification scores for models
trained and tested with TOL statistics. Our results confirm
that narwhal clicks had a larger difference between the 16
FIG. 5. (Color online) Mean power spectra for beluga (a) and narwhal (b) acoustic events. Mean spectra for individual acoustic events are shown in gray,
and the overall mean spectra across events are shown in black with the noise floor provided as a dashed line. The average noise spectrum was computed
using samples extracted from waveform clips that immediately preceded each click. Primary yaxes in (a) and (b) provide normalized power (dB) with
respect to the maximum and the secondary yaxes show corresponding power spectral density magnitudes (dB re 1 lPa
2
/Hz). Blue shaded areas in (a) and
(b) demarcate the frequency bandwidths of the 16, 25, and 40 kHz one-third octave bands. Violin plots with inset box plots show the distribution of the TOL
ratios (dB) between the 16 and 25 kHz and 25 and 40kHz one-third octave bands and the difference between these two ratios (i.e., 16:25 kHz TOL ratio sub-
tracted from 25:40 kHz TOL ratio) for beluga (c) and narwhal (d) acoustic events.
2366 J. Acoust. Soc. Am. 155 (4), April 2024 Zahn et al.
https://doi.org/10.1121/10.0025460
and 25 kHz one-third octave bands compared to belugas,
whereas beluga clicks had a larger difference between the
25 and 40 kHz bands compared to narwhals. OOB correct
classification rates were high for all RF models developed
(>99%; Table II). Similarly, RF models trained with data
from one site (i.e., Kong Oscar or Fisher Islands) correctly
predicted 100% of the acoustic events from the other site
(Table III). Since the beluga acoustic event sample size was
much smaller than the narwhal species class, beluga event
predictions had greater uncertainty. We expect that correct
prediction rates would remain high and confidence intervals
would narrow if there were a larger beluga event sample
size since the model would have more data for species
differentiation.
In summary, TOL ratios were strong predictors for
acoustic classification of Arctic toothed whale echolocation
signals. While echolocation parameters, such as peak
frequency, will vary depending on the sampling rate and fre-
quency response of the recording system, the 16 to 25 and
25 to 40 kHz TOL ratios will be unaffected as long as the
sampling rate is at least 96 kHz with a flat frequency
response between 14 and 45 kHz. However, ambient noise
levels will influence 16 and 25 kHz TOL estimates where
higher noise levels will decrease the relative magnitude
within these bands. Still, the TOL ratios between the 16 to
25 and 25 to 40 kHz bands were sufficiently large that
beluga and narwhal clicks remained differentiable across
two locations. Importantly, using TOL ratios for species
classification may not just be useful for belugas and nar-
whals. Employing TOL ratios for acoustic classification can
be applied to other odontocete species using selected one-
third octave bands that best capture click spectra variability.
C. Future recommendations
The present study marks an important step in automat-
ing beluga and narwhal acoustic detection for future passive
acoustic monitoring programs. Based on the results pre-
sented herein, we highlight that the lowermost part of echo-
location click spectra (<50 kHz) for narwhals and belugas
contain enough critical information for species classifica-
tion. Therefore, we recommend a minimum sampling rate of
96 kHz for the classification of these two species, although
recorders with higher sampling rates (400 kHz or more)
are needed for general signal characterization involving
parameters such as peak frequency and centroid frequency.
Given the sensory function of echolocation, acoustic proper-
ties of clicks appear to be less variable than social calls,
making them consistent metrics for species classification
across subpopulations. To fully implement an auto-detector
and classifier for recordings near glaciated coastlines, future
work must describe and classify transient ice sounds to iso-
late biotic and abiotic sounds. While this was outside of the
scope of the present study, we show highly accurate auto-
mated species predictions once cetacean clicks are
separated.
Arctic ecosystems are being altered by climate change
and trans-Arctic shipping routes will be used with greater
frequency in the next decade (Lannuzel et al., 2020;Meier
et al., 2014;Melia et al., 2016). Efforts to monitor changes
to ambient noise levels and endemic Arctic odontocete dis-
tributions are becoming increasingly important, especially
for the effective management of beluga and narwhal stocks
for harvest by Arctic communities in Canada and Greenland
(Hobbs et al., 2019). We verified that beluga and narwhal
clicks are differentiable, and moreover, discrete parameters
exist to automatically classify them at high success rates.
With the increasing prevalence of autonomous recorders
being used to monitor cetaceans globally, our results are
directly applicable to future passive acoustics research.
Going forward, we encourage sustained observations using
long-term passive acoustics from fixed platforms (e.g.,
moored HARP or SoundTrap) to track species occurrence of
TABLE III. RF confusion matrices for model predictions using the TOL
ratios between the 16 to 25 kHz and 25 to 40 kHz bands. Individual RF
models built for each site were tested with data from the other site to evalu-
ate model performance. Rows show the original acoustic event species
assignment and columns indicate classifier predictions. Model accuracy or
percentage of total acoustic events correctly classified (95% confidence
interval) and percent error are provided.
Beluga Narwhal Accuracy (95% CI) Error (95% CI)
Fisher Islands predicts Kong Oscar
Beluga 4 0 100% (39.8%–100%) 0% (0%–60.2%)
Narwhal 0 80 100% (95.5%–100%) 0% (0%–4.5%)
Overall 100% (95.7%–100%) 0% (0%–4.3%)
Kong Oscar predicts Fisher Islands
Beluga 12 0 100% (73.5%–100%) 0% (0%–26.5%)
Narwhal 0 105 100% (96.5%–100%) 0% (0%–3.5%)
Overall 100% (96.9%–100%) 0% (0%–3.1%)
TABLE II. RF confusion matrices for model development using the TOL
ratios between the 16 to 25 kHz and 25 to 40 kHz one-third octave bands.
Three RF models were trained using acoustic events from separate datasets:
(1) both sites, (2) Fisher Islands site, and (3) Kong Oscar site. Rows indicate
the original acoustic event species assignment and columns show predic-
tions by the classifier. OOB percent correct classification rates and percent
error (95% confidence interval) indicate model accuracy.
Beluga Narwhal
OOB accuracy
[95% confidence
interval (CI)] Error (95% CI)
Both sites
Beluga 16 0 100% (79.4%–100%) 0% (0%–60.2%)
Narwhal 1 184 99.5% (97.0%–100%) 0.5% (0%–3.0%)
Overall 99.5% (97.3%–100%) 0.5% (0%–2.7%)
Fisher Islands
Beluga 12 0 100% (73.5%–100%) 0% (0%–60.2%)
Narwhal 1 104 99.0% (94.8%–100%) 1.0% (5.2%–4.5%)
Overall 99.1% (95.3%–100%) 0.9% (4.7%–4.3%)
Kong Oscar
Beluga 4 0 100% (39.8%–100%) 0% (0%–60.2%)
Narwhal 0 80 100% (95.5%–100%) 0% (0%–4.5%)
Overall 100% (95.7%–100%) 0% (0%–4.3%)
J. Acoust. Soc. Am. 155 (4), April 2024 Zahn et al. 2367
https://doi.org/10.1121/10.0025460
resident and non-resident species and monitor increased
human activity in the Arctic.
SUPPLEMENTARY MATERIAL
See the supplementary material for additional informa-
tion. A catalogue of concatenated click spectrograms and
mean power spectra for all acoustic event data from this
study is available on Zenodo at https://doi.org/10.5281/
zenodo.10076260 (Zahn et al., 2023).
ACKNOWLEDGMENTS
This work was funded by the U.S. Office of Naval
Research (Award No. N00014-17-1-2774) and supported by
the NASA Oceans Melting Greenland EVS-2 mission. M.J.Z.
was partially supported by the Cooperative Institute for
Climate, Ocean, & Ecosystem Studies (CICOES) under
NOAA Cooperative Agreement NA20OAR4320271,
Contribution No. 2023-1312. M.S. and M.L. were supported
by the Danish Cooperation for Environment in the Arctic
(DANCEA, MST-2020-64692). We thank everyone who
contributed to the data collection. We are also grateful to
Jennifer L. K. McCullough for her assistance with PAMGuard,
Ben Cohen for Landsat 8 imagery support, and Alex Douglass
for his statistical advice. We thank Shannon Rankin for her
helpful input on previous versions of this manuscript.
AUTHOR DECLARATIONS
Conflict of Interest
The authors have no conflicts of interest to disclose.
DATA AVAILABILITY
Acoustic data from moorings is available upon request
from authors. Sea ice concentration ERA5 hourly data are sup-
plied by the Copernicus Climate Change Service Climate Data
Store at https://doi.org/10.24381/cds.adbb2d47. R and Python
code used to produce all analyses including classification mod-
els and figures for this manuscript are publicly available on
Zenodo: https://doi.org/10.5281/zenodo.10668629.
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