Marine Mammal Sounds Research Articles

Antarctic sonobuoy surveys for blue whales from 2006-2021 reveal contemporary distribution, changes over time, and paths to further our understanding of their distribution and biology

Seven passive acoustic surveys for marine mammal sounds were conducted by deploying sonobuoys along ship tracks during Antarctic voyages spanning years 2006-2021. These surveys included nearly 330° of longitude throughout Antarctic (south of 60°S) and sub-Antarctic (between 50-60°S) latitudes. Here, we summarise the presence of calls from critically endangered Antarctic blue whales (Balaenoptera musculus intermedia) detected on all seven of these surveys. We describe and compare the spatial distribution of detections of three different types of Antarctic blue whale calls: unit-A, Z-calls, and D-calls. Three sets of voyages partially overlapped spatially but in different years, providing three regions (Indian Sector, Dumont d’Urville Sea, Ross Sea) to investigate differences over time for these three different call types. The proportion of sonobuoys with calls present was significantly higher in the more recent years for seven of the 15 combinations of years, regions, and call type. The proportion of sonobuoys with calls present was significantly lower only for one of the 15 combinations (unit A in the Ross Sea between 2015 vs 2017), and not significantly different for the remaining seven pairwise comparisons. We discuss possible explanations for these observations including: differences in probability of detection, whale behaviour, whale distribution, and abundance. These explanations are not mutually exclusive and cannot yet be resolved without application of complex analytical methods and collection of additional data. Lastly, we discuss future work that could help clarify the contributions of each of these potential drivers of acoustic detection. We propose continued acoustic data collection, application of new analytical methods, and collection of other synergistic data from Antarctic blue whales on their feeding grounds as a basis for future work on this species. This could provide a cost effective and holistic means of monitoring their status after the effects of 20th century industrial whaling, as well as their responses to natural and anthropogenic changes to their main prey, Antarctic krill, and a changing climate.

Directional soundscapes in the Norwegian Seas observed with a coherent hydrophone array

Directional soundscaping is an efficient approach for examining marine ecosystems since it allows the study of living organisms, their behavior, and temporo-spatial interactions with other natural and man-made objects underwater, useful for ecosystem monitoring during offshore energy activities, maritime surveillance, and defense. A large aperture coherent hydrophone array was employed for remote sensing in the Norwegian and Barents Seas in Spring 2014. Extensive analysis have been conducted in post-processing of recorded hydrophone array data for automatic detection, bearing estimation, and classification of signals by different sound producers, such as whale calls, ship radiated noise, and fish sounds. Directional soundscaping through passive ocean acoustic waveguide remote sensing (POAWRS) provides bearing-time trajectories of signal detections that can be applied for geographical mapping. The received marine mammal sounds include vocalizations from baleen whales such as humpback, fin, and minke, and toothed whales, such as beluga, pilot, and sperm. We provide an insight to the vocalizations and behaviorial patterns of these whales in the Lofoten and Northern Finmark regions. The relative contributions of distinct sound sources, including whales, fish, and ships, to the directional soundscapes are quantified. We also examine their temporo-spatial dependences via geographic mapping of acoustic signals from distinct sound producers.

Acoustic diversity, bearing estimation, and localization of sound sources in shallow water and deep ocean off the U.S. Northeast coast using a coherent hydrophone array

An in-house developed 160-element large-aperture hydrophone array from Northeastern University was utilized for ocean monitoring in both the shallow waters of the Great South Channel (GSC) and the deep-water area off the continental slope south of Rhode Island in September 2021. Utilizing passive ocean acoustic waveguide remote sensing (POAWRS) technology, the system enabled real-time, broad-area surveillance of the oceanic environment. A novel multistage clustering was introduced for annotating the vast volume of underwater acoustic data, drawing inspiration from the human cognitive process of categorizing sounds. Initially, sounds are separated into broad groups, akin to a first human listening pass. Subsequent stages of clustering refine these categories, utilizing different sets of features to achieve a granularity of annotation that closely mirrors expert human annotation processes. It was found that the acoustic landscape of the GSC was primarily composed of diverse marine mammal sounds, such as fin whale 20 Hz pulses, humpback whale songs, minky whale buzz sequences, sperm whale and dolphin echolocation clicks, and unidentified whale calls. Conversely, the deep-water area off the continental slope featured predominantly fish sounds, chorus-like sounds, and dolphin vocalizations. Detailed analyses of the most prevalent vocalizations are presented, including their bearing-time trajectories and localizations.

Boundary Pass Underwater Listening Station—Data management and processing

The Boundary Pass Underwater Listening Station, sponsored by Transport Canada, Port of Vancouver and JASCO Applied Sciences, has continuously operated two compact tetrahedral hydrophone arrays beneath the busy shipping lanes leading to Vancouver, Canada since June 2020. The arrays are deployed 300 m apart on the seabed at 190 m depth and are cabled 2.6km to shore. A high sample rate of 512 kHz on all eight channels, with 24-bit samples, produces almost 400 TB/yr of raw acoustic data. Automated real-time data analysis calculates ambient noise statistics, detects marine mammal sounds, measures underwater radiated noise of individual ships, and creates spatial noise maps of the hulls of some vessels passing closely overhead. The derived data products are stored in an easy-to-maintain online database of just a few TB/yr. Results in graphic, tabular and compressed audio formats are made available to scientists and project managers through an intuitive web interface. The project follows international standards for ship noise and ambient noise calculations, so the results can be compared with measurements made elsewhere.

Azimuthal, range, and depth tracking of marine mammal sounds using a drifting acoustic vector sensor and hydrophone array platform

Deep-water acoustic tracking of marine mammals typically requires correlating hydrophone signals across both short and large-aperture hydrophone arrays. Here, we demonstrate how a single drifting, depth-controlled platform can obtain two and three-dimensional positional fixes of marine mammal sounds using an acoustic vector sensor and short-aperture arrays, both mounted on an autonomous drifting platform. In February and June 2023 two autonomous opto-acoustic drifters were deployed 50km off San Diego, CA at 250 m depth in 1030 m deep water. Both drifters were equipped with a CTD and acoustic recording system comprised of a 1.75 m aperture vertical hydrophone array, a tetrahedral hydrophone array, and either a 2-D Geospectrum M-35 or 3-D Wilcoxon VS-209 acoustic vector sensor. Throughout the two to three-day deployments, all acoustic sensors detected numerous marine mammal calls, including humpback whales and a pod of common dolphins. Estimates of azimuth and elevation were obtained from the simultaneously sampled acoustic vector sensor and hydrophone arrays, while multi-path and cross-platform processing were employed to extract range information. The results suggest that sparse deployments of drifting vector sensor platforms may be able to map biological distributions over spatial scales that would otherwise require larger numbers of hydrophone-only platforms. [Work supported by ONR TFO.]

Assessing marine mammal diversity in remote Indian Ocean regions, using an acoustic glider

Many observations collected from whaling logbooks or more recent satellite tags and acoustic surveys report that the Indian Ocean is a very important place for large baleen whales. They undergo long seasonal migrations from Southern feeding grounds to tropical and subtropical mating and breeding grounds. However, whether and where they stop to rest or feed during their long travels are poorly known. The Indian Ocean is also home to many odontocete species such as sperm whales, killer whales and multiple delphinid species. In this paper, we analyze passive acoustic data collected by an electric glider around two steep bathymetric features located in the Western sub-tropical Indian Ocean (Walters Shoal) and in the mid sub-tropical Indian Ocean (St. Paul and Amsterdam islands), both included in Important Marine Mammal Areas (IMMAs). The acoustic data were manually reviewed and annotated by two analysts. The aim of this experiment was to improve the knowledge on marine mammal presence in these little studied IMMAs. We found that bioacoustic activity was quite high in both monitored areas with 40% of the records containing marine mammal sounds in Walters Shoal and 70% in St. Paul and Amsterdam islands. Calls from Antarctic blue whales, Southwestern and Southeastern Indian Ocean pygmy blue whales, fin whales and an unidentified baleen whale were detected at one or both sites. Odontocete clicks and whistles were also recorded at both sites. The discussion puts these marine mammal acoustic detections back into the context of their seasonal and geographical presence already described by other studies in the Indian Ocean and makes hypotheses about the role of the two studied areas for marine mammals.

A Parallel Classification Model for Marine Mammal Sounds Based on Multi-Dimensional Feature Extraction and Data Augmentation.

Due to the poor visibility of the deep-sea environment, acoustic signals are often collected and analyzed to explore the behavior of marine species. With the progress of underwater signal-acquisition technology, the amount of acoustic data obtained from the ocean has exceeded the limit that human can process manually, so designing efficient marine-mammal classification algorithms has become a research hotspot. In this paper, we design a classification model based on a multi-channel parallel structure, which can process multi-dimensional acoustic features extracted from audio samples, and fuse the prediction results of different channels through a trainable full connection layer. It uses transfer learning to obtain faster convergence speed, and introduces data augmentation to improve the classification accuracy. The k-fold cross-validation method was used to segment the data set to comprehensively evaluate the prediction accuracy and robustness of the model. The evaluation results showed that the model can achieve a mean accuracy of 95.21% while maintaining a standard deviation of 0.65%. There was excellent consistency in performance over multiple tests.

Residual Learning for Marine Mammal Classification

The passive acoustic monitoring of marine mammals is an essential tool for researchers tracking the populations of individual species in threatened environments. Given the large quantity of audio data generated by passive acoustic arrays, it is desirable to automate the process of identifying marine mammals present in the recordings. Utilizing acoustic data from the William A. Watkins Marine Mammal Sounds Database, we present an approach using residual learning networks (ResNets) for classifying the marine mammal vocalizations of up to 32 species. We first determine the optimal methods for converting acoustic recordings into discrete spectrograms suitable for input into neural networks. A series of configurations for spectrographic window functions, preprocessing augmentations, and multi-channel spectrogram generation are examined. Each configuration’s spectrographic output is used to train a residual learning network. Its multi-class classification performance is ranked using the harmonic mean of precision and recall to calculate a weighted F1-score. Configurations specifying 512×256 spectrograms created with a Hann window of 1024 and utilizing horizontal roll demonstrate superior performance. We use the top-performing configurations to generate training data as input for a series of single and multi-channel residual neural networks. These networks are trained to high precision before evaluating their multi-class classification performance. A single-channel network performed the best, obtaining an F1-score of 0.867 with an AUC of 0.9281 on a 32-class classification task. Our multi-channel configuration obtained an F1-score of 0.846 with an AUC of 0.9169. While we demonstrate that networks may learn more information from multi-channel spectrographic inputs, we find that single-channel spectrograms offer superior classification performance overall.

A Monte Carlo approach to modelling detection ranges for killer whales in inshore waters of British Columbia, Canada

Passive underwater listening stations are often used to monitor the presence, distribution and movements of marine mammals. This requires an understanding of the distances at which marine mammal sounds can be detected at a location and at different times given varying ambient noise conditions. Here, we describe a Monte Carlo approach for determining call detection probabilities as a function of distance for networked underwater listening stations deployed by Fisheries and Oceans Canada in the Salish Sea to track endangered Southern Resident Killer Whales. We used ambient sound levels measured in situ, modelled propagation losses determined by two acoustic models, and applied Monte Carlo simulations to capture the variability in call source level and animal depth. Given that only some parts of the call frequency spectrum may be responsible for the maximum detection range, the analysis was carried out independently for consecutive 300 Hz frequency bands. Median detection range estimates ranged from 700 m at the noisiest time and location to 8 km at the quietest. A sensitivity analysis revealed that the frequency distribution of source levels used for the analysis was a major factor affecting detection range results, while differences in propagation losses between summer and winter were less important.

Long-term variations in calls produced by a potential blue whale species from the central Indian Ocean

The Comprehensive Nuclear-Test-Ban Treaty Organization hydrophones at Diego Garcia record many marine mammal sounds from the central Indian Ocean. A peculiar call between 20 and 40 Hz from a potential blue whale species, referred to as the Diego Garcia Downsweep (DGD), is regularly recorded in the dataset. The call consists of a set of tones that resemble a comb, followed by a downsweep. Long term spectral averages suggest that the call frequencies are drifting. Tracking the change in frequencies by mere visual observations from the spectrograms is however challenging because of interference from anthropogenic noises and other biophony. This talk presents signal processing methods to track the call-frequencies, across years 2002 to 2019. Energy detectors are initially used to identify, and then exclude acoustic interference from shipping and outliers from electronics. Following that, to isolate the DGDs, and cancel other biophony, the presentation constructs subspace detectors. Finally, the talk uses the spectrograms of the detections to track the change in frequencies for each day. The observations show that while the frequencies of the comb steadily increase, the downsweep decrease. There are new frequencies that appear, and some disappear. These changes are unlike behavior observed from other whales in the region.

Tom Norris: One scientist can make a lasting difference

Most of us in our youth have never heard of the term bioacoustics, let alone knew this could be a possible career option. Yet almost all of us in our youth have heard the fascinating sounds humpback whales make. Early on, it took an adventurous spirit, inquisitive mind, and perseverance to learn a wide variety of disciplines to study marine mammal bioacoustics. Even today, bioacoustics it typically a college course and not a full degree. Tom Norris was one of the few early scientists that helped shape what it takes to study bioacoustics and paved the way for others. With increasing concern about the potential impacts to marine mammals from noise in the ocean, there has been greater demand for those with a background in bioacoustics. Part of that overall interest has been using acoustics to passively listen and monitor marine mammal sounds, to learn more about the occurrence and distribution of these animals. Tom Norris’s efforts to create a better, more cost effective approach, helped shape the way that we use passive acoustic monitoring to study marine mammals today.

Detection and classification of marine mammal sounds using AlexNet with transfer learning

In this study, AlexNet with transfer learning was employed to automatically detect and classify the sounds of killer whales, long-finned pilot whales, and harp seals with widely overlapping living areas. Transfer learning was used to overcome the overfitting problem of deep network as the training samples was insufficient. A challenging dataset containing both target (the three marine mammal sounds) and non-target (ambient noise including ship noise, pulse interference, and man-made sounds, etc) sounds collected from different recording times, locations and devices was used to examine the performance of the proposed method, and the sounds used in the test dataset were completely independent of the training dataset. The overall accuracy of the trained detection and classification models reached 99.96% and 97.42% respectively. Importantly, each trained model took only 1.3 ms to detect or classify a single image. Furthermore, feature visualizations and strongest activations demonstrated that the proposed method learns the true differences between different marine mammal sounds rather than differences between different recording environments and devices. Therefore, all results show that the proposed method has excellent performance and great potential for practical application.

An open access dataset for developing automated detectors of Antarctic baleen whale sounds and performance evaluation of two commonly used detectors

Since 2001, hundreds of thousands of hours of underwater acoustic recordings have been made throughout the Southern Ocean south of 60° S. Detailed analysis of the occurrence of marine mammal sounds in these circumpolar recordings could provide novel insights into their ecology, but manual inspection of the entirety of all recordings would be prohibitively time consuming and expensive. Automated signal processing methods have now developed to the point that they can be applied to these data in a cost-effective manner. However training and evaluating the efficacy of these automated signal processing methods still requires a representative annotated library of sounds to identify the true presence and absence of different sound types. This work presents such a library of annotated recordings for the purpose of training and evaluating automated detectors of Antarctic blue and fin whale calls. Creation of the library has focused on the annotation of a representative sample of recordings to ensure that automated algorithms can be developed and tested across a broad range of instruments, locations, environmental conditions, and years. To demonstrate the utility of the library, we characterise the performance of two automated detection algorithms that have been commonly used to detect stereotyped calls of blue and fin whales. The availability of this library will facilitate development of improved detectors for the acoustic presence of Southern Ocean blue and fin whales. It can also be expanded upon to facilitate standardization of subsequent analysis of spatiotemporal trends in call-density of these circumpolar species.

Caribbean Sea Soundscapes: Monitoring Humpback Whales, Biological Sounds, Geological Events, and Anthropogenic Impacts of Vessel Noise

Assessing marine soundscapes provides an understanding of the biological, geological and anthropogenic composition of a habitat, including species diversity, community composition, and human impacts. For this study, nine acoustic recorders were deployed between December 2016 and June 2017 off six Caribbean islands in several Marine Parks: the Dominican Republic (DR), St. Martin (SM), Guadeloupe east and west (GE, GW), Martinique (MA), Aruba (AR), and Bonaire (BO). Humpback whale song was recorded at five sites on four islands (DR, SM, GE, GW and MA) and occurred on 49 to 93% of recording days. Song appeared first at the DR site and began 4 to 6 weeks later at GE, GW and MA. No song was heard in AR and BO, the southernmost islands. A two-week period was examined for the hourly presence of vessel noise and the number and duration of ship passages. Hourly vessel presence ranged from low (20% - DR, 30% - SM), medium (52% -MA, 54% - BO, 77% - GE) to near continuous (99% - GW; 100% - AR). Diurnal patterns were observed at BO, GE, and MA with few to no vessels present during night time hours, possibly reflecting the activity of recreational craft and fishing vessels. At the DR and GW sites, vessel traffic was ubiquitous for most of the day, likely reflecting heavy cruise ship and container ship presence. Soundscapes were diverse across islands with persistent fish choruses, sporadic sperm whale (Physeter macrocephalus) and dolphin (Delphinidae) presence at BO, minke whales (Balaenoptera acutorostrata) from late December to late February at MA and an earthquake recorded across all sites. These analyses provide an important first step in characterizing the health and species richness in Caribbean marine parks and demonstrate a surprising high anthropogenic foot print. Vessel traffic in particular contributes adversely to marine soundscapes, masking marine mammal sounds, potentially changing typical animal behavior and raising the risk of ship strike.

The central role of Woods Hole Oceanographic Institution (WHOI) in marine mammal bioacoustics

WHOI researchers pioneered marine mammal bioacoustics. Schevill and Lawrence in 1949 made the first recordings of marine mammals, beluga whales, and in 1956 first demonstrated cetacean echolocation. Schevill later concentrated on the taxonomy and behavior of cetaceans while Watkins developed the first portable high-frequency recorder and passive and active acoustic tracking systems. Ray, Watkins, and Schevill in 1969 presented the first evidence of song in a marine mammal, the bearded seal, linked to the behavior of males in the breeding season. Watkins’ papers on vocalizations informed a generation of bioacousticians on how to interpret sonograms. He was one of the first to use the Navy’s SOSUS hydrophone array to track cetacean movements. Schevill and Watkins also inspired the research of others, both in recoding marine mammal sounds and in interpreting sound in behavior. Tyack led the development of the D-Tag, which simultaneously records vocalizations and movements of cetaceans. Ketten used CAT scans to develop hearing models for cetaceans. Sayigh advanced knowledge of delphnid signature whistles and Fristrup further developed the concept of soundscapes. Watkins and Schevill’s database of more than 20,000 vocalizations from 70 marine mammal species now resides at the New Bedford Whaling Museum, freely available to the public.WHOI researchers pioneered marine mammal bioacoustics. Schevill and Lawrence in 1949 made the first recordings of marine mammals, beluga whales, and in 1956 first demonstrated cetacean echolocation. Schevill later concentrated on the taxonomy and behavior of cetaceans while Watkins developed the first portable high-frequency recorder and passive and active acoustic tracking systems. Ray, Watkins, and Schevill in 1969 presented the first evidence of song in a marine mammal, the bearded seal, linked to the behavior of males in the breeding season. Watkins’ papers on vocalizations informed a generation of bioacousticians on how to interpret sonograms. He was one of the first to use the Navy’s SOSUS hydrophone array to track cetacean movements. Schevill and Watkins also inspired the research of others, both in recoding marine mammal sounds and in interpreting sound in behavior. Tyack led the development of the D-Tag, which simultaneously records vocalizations and movements of cetaceans. Ketten used CAT scans ...

Advanced methods for passive acoustic detection, classification, and localization of marine mammals

For effective long-term passive acoustic monitoring of large data sets, automated algorithms provide the ability to detect, classify, and locate (DCL) marine mammal vocalizations. Several DCL algorithms were developed and enhanced, with emphasis on methods robust to non-Gaussian and non-stationary noise sources. (1) A subspace model was developed to separate odontocete clicks from noise sounds. (2) A multi-class support vector machine was improved by resolving confusion among species’ overlapping-frequency clicks, and a beaked whale buzz class developed. (3) For dolphin whistles, shape-related features, extractable automatically, were shown to carry species-specific information. (4) Equipment and site differences were discovered to affect Gaussian mixture model classifiers, and methods were developed to mitigate these differences. (5) A nearest-neighbor approach to detection association and 3D localization across multiple phones with multiple arrivals was developed (and applied to beaked whales) using time-difference-of-arrival (TDOA) hyperbolic methods, retaining TDOAs with fewer than the usual three detections and using associations between a given phone’s detections with nearest neighbors. (6) Minke whale “boing” frequency estimates were improved to differentiate individuals, and a kinematic tracking algorithm was developed. (7) A generalized-power-law detector for humpback whales was improved. (8) A software interface for detection was developed, then tested by sending data from Ishmael to a detection process in MATLAB.

The influence of sea ice, wind speed and marine mammals on Southern Ocean ambient sound.

This paper describes the natural variability of ambient sound in the Southern Ocean, an acoustically pristine marine mammal habitat. Over a 3-year period, two autonomous recorders were moored along the Greenwich meridian to collect underwater passive acoustic data. Ambient sound levels were strongly affected by the annual variation of the sea-ice cover, which decouples local wind speed and sound levels during austral winter. With increasing sea-ice concentration, area and thickness, sound levels decreased while the contribution of distant sources increased. Marine mammal sounds formed a substantial part of the overall acoustic environment, comprising calls produced by Antarctic blue whales (Balaenoptera musculus intermedia), fin whales (Balaenoptera physalus), Antarctic minke whales (Balaenoptera bonaerensis) and leopard seals (Hydrurga leptonyx). The combined sound energy of a group or population vocalizing during extended periods contributed species-specific peaks to the ambient sound spectra. The temporal and spatial variation in the contribution of marine mammals to ambient sound suggests annual patterns in migration and behaviour. The Antarctic blue and fin whale contributions were loudest in austral autumn, whereas the Antarctic minke whale contribution was loudest during austral winter and repeatedly showed a diel pattern that coincided with the diel vertical migration of zooplankton.

Seasonal marine mammal occurrence detected from passive acoustic monitoring in Scott Inlet, Nunavut, Canada

Monitoring Arctic wildlife is important due to the increasing potential impact of environmental and industrial changes. Passive acoustic monitoring (PAM) can be an important tool for wildlife management through observations of several species simultaneously along with abiotic elements such as industrial activity. Here, we use PAM methods to monitor the occurrence of marine mammals over an 8-month period (Oct 2012–May 2013) in Scott Inlet, Nunavut. When marine mammals were present, we investigated possible environmental correlates. Sea ice coverage strongly influenced detections of marine mammal sounds: narwhals and bowhead whales were only present before full ice cover occurred in the fall, while bearded seals and walruses could only be detected after ice formation. Tidal phase, time of the day, water temperature at 300 m depth, and air temperature all influenced detections of narwhal clicks. This PAM study provides a baseline measure for the presence of marine mammals over the fall to spring in Scott Inlet. Long-term PAM in the same location would allow us to document changes in the phenology of marine mammals at this site in relation to environmental changes.

Visual acoustic analysis techniques for North Atlantic right whale sounds

Passive acoustic monitoring (PAM) is becoming a more widely accepted tool in mitigating the potential impact of man-made noise on marine mammals. Many marine mammals, and in particular cetaceans (whales and dolphins), use sound to communicate, navigate, forage, and avoid predators. Automated vocalization detectors have been in development for many years. Validation is achieved through aural/visual verification by analysts examining acoustic data in both the time and frequency domains. However, many factors also influence the ability of the analyst to properly detect and classify marine mammal sounds. First, a priori knowledge of the acoustic signature and the analysts experience are critical factors. Assuming the a priori knowledge exists, the ability to correctly assess the presence of call is dependent on the signal processing and displays available. Frequency resolution, temporal integration, and normalization either enhance or inhibit the ability to make the assessment. The signal processing parameter...

Applications of an adaptive back-propagation method for passive acoustic localizations of marine mammal sounds

An adaptive back-propagation localization method utilizing the dispersion relation of the acoustic modes of low-frequency sound signals is reviewed in this talk. This method employs an adaptive array processing technique (the maximum a posteriori mode filter) to extract the acoustic modes of sound signals, and it is capable of separating signals from noisy data. The concept of the localization algorithm is to back-propagate modes to a location where the modes align with each other. Gauss-Markov inverse theory is applied to make the normal mode back-propagator adaptive to the signal-to-noise ratio (SNR). When the SNR is high, the localization procedure will push the algorithm to achieve high resolution. On the other hand, when the SNR is low, the procedure will try to retain its robustness and reduce the noise effects. Examples will be shown in the talk to demonstrate the localization performance with comparisons to other methods. Applications to baleen whale sounds collected in Cape Cod Bay, Massachusetts, will also be presented. Lastly, population density estimation using this passive acoustic localization method will be discussed.