Whale Calls Research Articles

Presence of Southeast Pacific blue whales (Balaenoptera musculus) off South Georgia in the South Atlantic Ocean

AbstractThe Southeast Pacific (SEP) or Chilean blue whale population is largely distributed between Northern Chilean Patagonia and the Eastern Tropical Pacific. Off South Georgia (SG), the majority of blue whales caught were Antarctic blue whales, but recent genetic and acoustic records indicate the possible presence of SEP individuals. To assess the presence of SEP blue whales off SG, we carried out new analyses of acoustic data previously reported as containing pygmy blue whale song and analyzed the length frequencies from 20th century catch data. The month of acoustic data reported to contain the presumed pygmy blue whale song was examined (August 2006), and 13 days were found to have songs which visually and quantitatively matched SEP2 songs from Northern Chilean Patagonia. The fundamental frequency of SEP2 song off SG, however, was not in line with the predicted frequency shift trend of the SEP2 songs in the SEP. A mixture analysis of lengths in historic whaling catches indicated that 3.3% of the catches from SG could be SEP blue whales, although this declined to 0.6%, 95% CI [0.0, 2.6] when fit to nonrounded lengths. Our results suggest that any SEP blue whales off SG are likely rare vagrants, at the edge of an endangered population.

Changes of Oceanic Conditions Drive Chagos Whale Migration Patterns in the Central Indian Ocean

Marine ecosystems are experiencing rapid shifts under climate change scenarios and baleen whales are vulnerable to environmental change, although not all impacts are yet clear. We identify how the migration behaviour of the Chagos whale, likely a pygmy blue whale (Balaenoptera musculus brevicauda), has changed in association with shifts in environmental factors. We used up to 18 years of continuous underwater acoustic recordings to analyse the relationships between whale acoustic presence and sea surface temperature (SST), chlorophyll-a concentration, El-Niño Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD). We compared these relationships between two independent sites Diego Garcia southeast (DGS) and Diego Garcia northwest (DGN) where Chagos whales are detected and are suspected to move interannually across the Chagos-Laccadive ridge. We showed that the number of whale songs detected increased on average by 7.7% and 12.6% annually at DGS and DGN respectively. At the DGS site, Chagos whales shifted their arrival time earlier by 4.2 ± 2.0 days/year ± SE and were detected for a longer period by 7.3 ± 1.2 days/year ± SE across 18 years. A larger number of songs were detected during periods of higher chlorophyll-a concentration, and with positive IOD phases. At the DGN site, we did not see an earlier shift in arrival and songs were not detected for a longer period across the 13 years. Whale presence at DGN had a weaker but opposite relationship with chlorophyll-a and IOD. The oceanic conditions in the Indian Ocean are predicted to change under future climate scenarios and this will likely influence Chagos whale migratory behaviour. Understanding how environmental factors influence whale movement patterns can help predict how whales may respond to future environmental change. We demonstrate the value of long-term acoustic monitoring of marine fauna to determine how they may be affected by changing environmental conditions.

Mathematical models of long term evolution of blue whale song types’ frequencies

The linear decrease in the frequency of blue whale songs around the world is, to date, an unexplained phenomenon. We show it can be reproduced by a mathematical model considering two antagonistic behavioral trends: first, a bias towards conformity in the song, and second, a tendency to try and sing lower than the other whales. We check the robustness of our model by considering some more complex premises. First, different hierarchical relations between the singers are explored, adapting methods used in the flocking motion studies. Then a population-dependant simulation shows that even considering the gradual addition of new whales, the evolution is still globally linear. Finally, we show that intra-annual variations surging from different causes can be naturally incorporated into the model. We then conclude that, unlike other explanations, a cultural hypothesis seems compatible with the observed linearity of the blue whales’s songs frequency shift.

Killer Whale (Orcinus orca) Pulsed Calls in the Eastern Canadian Arctic


 Killer whales (Orcinus orca) produce a variety of acoustic signal types used for communication: clicks, whistles, and pulsed calls. Discrete pulsed calls are highly stereotyped, repetitive, and unique to individual pods found around the world. Discriminating amongst pod specific calls can help determine population structure in killer whales and is used to track pod movements around oceans. Killer whale presence in the Canadian Arctic has increased substantially, but we have limited understanding of their ecology, movements, and stock identity. Two autonomous passive acoustic monitoring (PAM) hydrophones were deployed in the waters of Eclipse Sound and Milne Inlet, in northern Baffin Island, Nunavut, Canada, in August and September 2017. Eleven killer whale pulsed call types, three multiphonic and eight monophonic, are proposed and described using manual whistle contour extraction and feature normalization. Automated detection of echolocation clicks between 20 and 48 kHz demonstrated little to no overlap between killer whale calls and echolocation presumed to be narwhal, which suggests that narwhal remain audibly inconspicuous when killer whales are present. Describing the acoustic repertoire of killer whales seasonally present in the Canadian Arctic will aid in understanding their acoustic behaviour, seasonal movements, and ecological impacts. The calls described here provide a basis for future acoustic comparisons across the North Atlantic and aid in characterizing killer whale demographics and ecology, particularly for pods making seasonal incursions into Arctic waters.

Acoustic localization, validation, and characterization of Rice's whale calls.

The recently named Rice's whale in the Gulf of Mexico is one of the most endangered whales in the world, and improved knowledge of spatiotemporal occurrence patterns is needed to support their recovery and conservation. Passive acoustic monitoring methods for determining spatiotemporal occurrence patterns require identifying the species' call repertoire. Rice's whale call repertoire remains unvalidated though several potential call types have been identified. This study uses sonobuoys and passive acoustic tagging to validate the source of potential call types and to characterize Rice's whale calls. During concurrent visual and acoustic surveys, acoustic-directed approaches were conducted to obtain visual verifications of sources of localized sounds. Of 28 acoustic-directed approaches, 79% led to sightings of balaenopterid whales, of which 10 could be positively identified to species as Rice's whales. Long-moan calls, downsweep sequences, and tonal-sequences are attributed to Rice's whales based on these matches, while anthropogenic sources are ruled out. A potential new call type, the low-frequency downsweep sequence, is characterized from tagged Rice's whale recordings. The validation and characterization of the Rice's whale call repertoire provides foundational information needed to use passive acoustic monitoring for better understanding and conservation of these critically endangered whales.

An automated piecewise synthesis method for cetacean tonal sounds based on time-frequency spectrogram.

Bionic signal waveform design plays an important role in biological research, as well as bionic underwater acoustic detection and communication. Most conventional methods cannot construct high-similarity bionic waveforms to match complex cetacean sounds or easily modify the time-frequency structure of the synthesized bionic signals. In our previous work, we proposed a synthesis and modification method for cetacean tonal sounds, but it requires a lot of manpower to construct each bionic signal segment to match the tonal sound contour. To solve these problems, an automated piecewise synthesis method is proposed. First, based on the time-frequency spectrogram of each tonal sound, the fundamental contour and each harmonic contour of the tonal sound is automatically recognized and extracted. Then, based on the extracted contours, four sub power frequency modulation bionic signal models are combined to match cetacean sound contours. Finally, combining the envelopes of the fundamental frequency and each harmonic, the synthesized bionic signal is obtained. Experimental results show that the Pearson correlation coefficient (PCC) between all true cetacean sounds and their corresponding bionic signals are higher than 0.95, demonstrating that the proposed method can automatically imitate all kinds of simple and complex cetacean tonal sounds with high similarity.

Song complexity is maintained during inter-population cultural transmission of humpback whale songs

Among animal species, the songs of male humpback whales (Megaptera novaeangliae) are a rare example of social learning between entire populations. Understanding fine-scale similarity in song patterns and structural features will better clarify how accurately songs are learned during inter-population transmission. Here, six distinct song types (2009–2015) transmitted from the east Australian to New Caledonian populations were quantitatively analysed using fine-scale song features. Results found that New Caledonian whales learned each song type with high accuracy regardless of the pattern’s complexity. However, there were rare instances of themes (stereotyped patterns of sound units) only sung by a single population. These occurred more often in progressively changing ‘evolutionary’ songs compared to rapidly changing ‘revolutionary’ songs. Our results suggest that populations do not need to reduce complexity to accurately learn song patterns. Populations may also incorporate changes and embellishments into songs in the form of themes which are suggested to be learnt as distinct segments. Maintaining complex song patterns with such accuracy suggests significant acoustic contact, supporting the hypothesis that song learning may occur on shared feeding grounds or migration routes. This study improves the understanding of inter-population mechanisms for large-scale cultural transmission in animals.

The influence of sea ice on the detection of bowhead whale calls

Bowhead whales (Balaena mysticetus) face threats from diminishing sea ice and increasing anthropogenic activities in the Arctic. Passive acoustic monitoring is the most effective means for monitoring their distribution and population trends, based on the detection of their calls. Passive acoustic monitoring, however, is influenced by the sound propagation environment and ambient noise levels, which impact call detection probability. Modeling and simulations were used to estimate detection probability for bowhead whale frequency-modulated calls in the 80–180 Hz frequency band with and without sea ice cover and under various noise conditions. Sound transmission loss for bowhead calls is substantially greater during ice-covered conditions than during open-water conditions, making call detection ~ 3 times more likely in open-water. Estimates of daily acoustic detection probability were used to compensate acoustic detections for sound propagation and noise effects in two recording datasets in the northeast Chukchi Sea, on the outer shelf and continental slope, collected between 2012 and 2013. The compensated acoustic density suggests a decrease in whale presence with the retreat of sea ice at these recording sites. These results highlight the importance of accounting for effects of the environment on ambient noise and acoustic propagation when interpreting results of passive acoustic monitoring.

Cultural Confusion: Parsimony, Social Learning, and Humpback Whales

In his viewpoint against the general zeitgeist of humpback whale song analyses, Mercado (2022) makes several problematic arguments Although minimized, his arguments are based in parsimony; Mercado is assuming that the most efficient answers are the most likely to be true – an argument not always supported by evolutionary data. Parsimony in Mercado’s arguments can be divided into parsimony of mechanism (which mechanism is “simplest”) and parsimony of explanation (one mechanism is better than two). Both of these arguments are flawed. The first because social learning is not as rare or complex as has been previously claimed and the second because complex systems, particularly vocal systems, frequently utilize more than one mechanism of change. Mercado’s data do provide interesting contrasts to the assumptions made by humpback whale researchers, but his use of rhetoric and straw man arguments undermine the potential impact of those data.

The Humpback’s New Songs: Diverse and Convergent Evidence Against Vocal Culture via Copying in Humpback Whales

Singing humpback whales constantly modify their songs over hours, days, months, and years, throughout their adult lives. Intriguingly, humpbacks appear to vary songs in concert, with most singers in a population producing similar songs at any given time. The convergent vocal dynamics of singing humpbacks have convinced many that songs are vocal customs, passed from singer to singer through vocal imitation. This interpretation has recently been challenged, however, by the discovery that singers not in acoustic contact may sing highly similar songs, and also appear to change their songs along similar trajectories. How could singers that cannot hear each other culturally conform? Here, it is argued that the changes humpback whales make to songs are inconsistent with either communal copying or competitive improvisation. Instead, singers apar to be continuously morphing the acoustic properties of songs in predictable ways both within and across songs, even in the absence of cross-population interactions. There is no direct evidence that singing humpback whales learn songs by copying other singers. The fact that groups of singers change songs in similar ways is not evidence of vocal imitation, cultural transmission, or cultural evolution. So called “cultural revolutions” in humpback whale songs, which have been touted as the clearest and most impressive evidence of culture in any nonhuman animal, actually provide evidence against vocal culture in humpback whales. Vocal complexity and convergence can arise through mechanisms other than cultural transmission via vocal imitation, and in the case of humpback whales, genetic predispositions and ecological conditions may be more relevant to determining how singers collectively change songs over time.

The Beat Goes On: Humpback Whale Song Seasonality in Antarctic and South African Waters

Little is known of the movements and seasonal occurrence of humpback whales (Megaptera novaeangliae) of South Africa and the Antarctic, populations once brought to near extinction by historic commercial whaling. We investigated the seasonal occurrence and diel-vocalizing pattern of humpback whale songs off the west coast of South Africa (migration route and opportunistic feeding ground) and the Maud Rise, Antarctica (feeding ground), using passive acoustic monitoring data collected between early 2014 and early 2017. Data were collected using acoustic autonomous recorders deployed 200-300 m below the sea surface in waters 855, 1,118 and 4,400 m deep. Acoustic data were manually analyzed for humpback whale vocalizations. While non-song calls were never identified, humpback whale songs were detected from June through December in South African waters, with a peak in percentage of acoustic occurrence around September/October in the austral spring. In Antarctic waters, songs were detected from March through May and in July (with a peak occurrence in April) where acoustic occurrence of humpback whales was negatively correlated to distance to the sea ice extent. Humpback whales were more vocally active at night than in the day at all recording sites. Detection range modelling indicates that humpback whale vocalizations could be detected as far as 18 and 45 km from recorders in South African and Antarctic waters, respectively. This study provides a multi-year description of the offshore acoustic occurrence of humpback whales off the west coast of South Africa and Maud Rise, Antarctica, regions that should continue to be monitored to understand these recovering populations.

Changes in gray whale phenology and distribution related to prey variability and ocean biophysics in the northern Bering and eastern Chukchi seas.

Changes in gray whale (Eschrichtius robustus) phenology and distribution are related to observed and hypothesized prey availability, bottom water temperature, salinity, sea ice persistence, integrated water column and sediment chlorophyll a, and patterns of wind-driven biophysical forcing in the northern Bering and eastern Chukchi seas. This portion of the Pacific Arctic includes four Distributed Biological Observatory (DBO) sampling regions. In the Bering Strait area, passive acoustic data showed marked declines in gray whale calling activity coincident with unprecedented wintertime sea ice loss there in 2017-2019, although some whales were seen there during DBO cruises in those years. In the northern Bering Sea, sightings during DBO cruises show changes in gray whale distribution coincident with a shrinking field of infaunal amphipods, with a significant decrease in prey abundance (r = -0.314, p<0.05) observed in the DBO 2 region over the 2010-2019 period. In the eastern Chukchi Sea, sightings during broad scale aerial surveys show that gray whale distribution is associated with localized areas of high infaunal crustacean abundance. Although infaunal crustacean prey abundance was unchanged in DBO regions 3, 4 and 5, a mid-decade shift in gray whale distribution corresponded to both: (i) a localized increase in infaunal prey abundance in DBO regions 4 and 5, and (ii) a correlation of whale relative abundance with wind patterns that can influence epi-benthic and pelagic prey availability. Specifically, in the northeastern Chukchi Sea, increased sighting rates (whales/km) associated with an ~110 km (60 nm) offshore shift in distribution was positively correlated with large scale and local wind patterns conducive to increased availability of krill. In the southern Chukchi Sea, gray whale distribution clustered in all years near an amphipod-krill 'hotspot' associated with a 50-60m deep trough. We discuss potential impacts of observed and inferred prey shifts on gray whale nutrition in the context of an ongoing unusual gray whale mortality event. To conclude, we use the conceptual Arctic Marine Pulses (AMP) model to frame hypotheses that may guide future research on whales in the Pacific Arctic marine ecosystem.

Extraction of Energy Characteristics of Blue Whale Vocalizations Based on Empirical Mode Decomposition.

This study extracts the energy characteristic distributions of the intrinsic mode functions (IMFs) and residue functions (RF) for a blue whale sound signal, with empirical mode decomposition (EMD) as the basic theoretical framework. A high-resolution marginal frequency characteristics extraction method, based on EMD with energy density intensity (EDI) parameters for blue B call vocalizations, was proposed. The extraction algorithm included six steps: EMD, energy analysis, marginal frequency (MF) analysis with EDI parameters, feature extraction (FE), classification, and Hilbert spectrum (HS) analysis. The blue whale sound sources were obtained from the website of the Scripps Whale Acoustics Lab of the University of California, San Diego, USA. The source is a type of B call with a time duration of 46.65 s, from which 59 analysis samples with a time duration of 180 ms were taken. The average energy distribution ratios of the IMF1, IMF2, IMF3, IMF4, and RF are 49.06%, 20.58%, 13.51%, 10.94% and 3.84%, respectively. New classification criteria and EDI parameters were proposed to extract the blue whale B call vocalization (BWBCV) characteristics. The analysis results show that the main frequency bands of the signal are distributed at 41–43 Hz in the MF of IMF1 for Class I BWBCV and 11–13 Hz in the MF of IMF2 for Class II BWBCV, respectively.

Update on frequency decline of Northeast Pacific blue whale (Balaenoptera musculus) calls.

Worldwide, the frequency (pitch) of blue whale (Balaenoptera musculus) calls has been decreasing since first recorded in the 1960s. This frequency decline occurs over annual and inter-annual timescales and has recently been documented in other baleen whale species, yet it remains unexplained. In the Northeast Pacific, blue whales produce two calls, or units, that, when regularly repeated, are referred to as song: A and B calls. In this population, frequency decline has thus far only been examined in B calls. In this work, passive acoustic data collected in the Southern California Bight from 2006 to 2019 were examined to determine if A calls are also declining in frequency and whether the call pulse rate was similarly impacted. Additionally, frequency measurements were made for B calls to determine whether the rate of frequency decline is the same as was calculated when this phenomenon was first reported in 2009. We found that A calls decreased at a rate of 0.32 Hz yr-1 during this period and that B calls were still decreasing, albeit at a slower rate (0.27 Hz yr-1) than reported previously. The A call pulse rate also declined over the course of the study, at a rate of 0.006 pulses/s yr-1. With this updated information, we consider the various theories that have been proposed to explain frequency decline in blue whales. We conclude that no current theory adequately accounts for all aspects of this phenomenon and consider the role that individual perception of song frequency may play. To understand the cause behind call frequency decline, future studies might want to explore the function of these songs and the mechanism for their synchronization. The ubiquitous nature of the frequency shift phenomenon may indicate a consistent level of vocal plasticity and fine auditory processing abilities across baleen whale species.

Cetacean acoustic monitoring across the Hawaiian archipelago: Building on Whitlow Au’s legacy

The Hawaiian islands are home to more than 20 species of cetaceans and are the principal breeding ground of the north Pacific humpback whale population. The archipelago stretches more than 2500 km from Hawaii Island to Kure Atoll, creating a significant challenge for monitoring the occurrence and distribution of cetaceans across such a vast range. To meet this challenge, the National Oceanic and Atmospheric Administration and the U.S. Navy have been engaged in a three-year effort to monitor the marine soundscape of the Hawaiian archipelago known as the SanctSound Project. Bottom-moored acoustic recorders were deployed at multiple locations across the Hawaiian Islands Humpback Whale National Marine Sanctuary and the Papahānaumokuākea Marine National Monument to examine the occurrence of humpback whales and odontocetes based on the relative prevalence of their acoustic signaling. Anthropogenic sound sources were also studied to understand how these co-occur with cetaceans. Substantial spatial and temporal variability was observed in the prevalence of whale song and dolphin acoustic activity across locations with high cetacean presence sometimes overlapped with elevated anthropogenic activity. This work helps expand our understanding of how cetaceans use the archipelago and builds on the legacy of Whitlow Au, who pioneered cetacean acoustic monitoring in Hawaii.

Assessing intra-individual consistency in humpback whale song production using animal-borne acoustic recorders

Studying individual variation in vocal behavior can provide insight into its functions, stability, and mechanisms. Collecting such data at the scale of the individual can be facilitated using animal-borne tags. Here, we use archival suction-cup acoustic recording tags to investigate intra-individual variation in male humpback whale song production. Humpback whale song is a complex and hierarchically structured vocal sequence of 4-7 repeated phrases that are comprised of different units. Repeated songs are termed song sessions. To investigate how consistent song production is within the song session of an individual whale, we deployed suction cup-attached acoustic recording tags on humpback whales in the Hawaiian Islands Humpback Whale National Marine Sanctuary during the 2018–2022 breeding seasons. We analyzed tag data from nine whales to assess intra-individual variation in song structure and syntax as well as differences in the acoustic properties of song units across each song iteration. Each tag contained between 4 and 24 song iterations, and song session recordings varied between 45 minutes and 5 hours. Across individuals, the most variable song iterations occurred at the beginning of a song session. All individuals showed variation in syntax and unit production throughout a session; however, some singers were more consistent than others.

Marine mammal detections on the Chukchi Plateau 2009-2020.

The Arctic Ice Monitoring (AIM) observatory has been maintained on the Chukchi Plateau at 75.1° N 168.0° W nearly continuously since 2003. The AIM site consists of a submerged mooring that, since October 2008, has been instrumented with a passive acoustic recorder to sample ambient sound, with a focus on marine mammal detections in the High Arctic. Year-long data sets for 2009, 2012, and 2014-2020 were analyzed for the presence of signals from Arctic species including bowhead and beluga whales, bearded seals, and walrus. Calls from subarctic ribbon seals were commonly detected in autumn months, suggesting they have expanded their distribution much further northward. Killer whale calls were detected in recent years providing evidence that they have moved further north into the Pacific Arctic. No other subarctic cetaceans were heard. Year-round passive acoustic sampling of sounds produced by marine mammals over a decadal timescale has enhanced our understanding of how climate-driven changes in biodiversity are affecting even the very High Arctic.

Combining neural network and sequence analyses to determine animal call repertoires

Classifying frequency modulation patterns in animal signals is a challenging task depending on the level of stereotypy of call types. Not only do graded transitions between types create problems for defining them, but animals also vary single parameters within types without changing the overall shape of a modulation. One example for the latter is the variation in overall duration within call types of delphinids. Deecke et al. (1999) developed an adaptive resonance theory neural network for killer whale calls to standardize classification for dialect comparisons. This was later modified to make it usable for delphinid whistles by Deecke and Janik (2006) and is available as free software under the name ARTWARP. It creates categories by comparing frequency contours while time warping them to allow for variation in overall duration. Average frequency contours of all resulting call types are provided to describe repertoires once classification is complete. ARTWARP has recently been further improved to expand its analysis outputs and speed and can be used for the classification of any animal signals with a tonal component. Tests of ARTWARP in combination with the SIGID sequence analysis for signature whistles (Janik et al., 2013) provides an accurate repertoire of delphinid calls.

Parameterizing animal sounds and motion with animal-attached tags to study acoustic communication

Stemming from the traditional use of field observers to score states and events, the study of animal behaviour often relies on analyses of discrete behavioural categories. Many studies of acoustic communication record sequences of animal sounds, classify vocalizations, and then examine how call categories are used relative to behavioural states and events. However, acoustic parameters can also convey information independent of call type, offering complementary study approaches to call classifications. Animal-attached tags can continuously sample high-resolution behavioural data on sounds and movements, which enables testing how acoustic parameters of signals relate to parameters of animal motion. Here, we present this approach through case studies on wild common bottlenose dolphins (Tursiops truncatus). Using data from sound-and-movement recording tags deployed in Sarasota (FL), we parameterized dolphin vocalizations and motion to investigate how senders and receivers modified movement parameters (including vectorial dynamic body acceleration, “VeDBA”, a proxy for activity intensity) as a function of signal parameters. We show that (1) VeDBA of one female during consortships had a negative relationship with centroid frequency of male calls, matching predictions about agonistic interactions based on motivation-structural rules; (2) VeDBA of four males had a positive relationship with modulation rate of their pulsed vocalizations, confirming predictions that click-repetition rate of these calls increases with agonism intensity. Tags offer opportunities to study animal behaviour through analyses of continuously sampled quantitative parameters, which can complement traditional methods and facilitate research replication. Our case studies illustrate the value of this approach to investigate communicative roles of acoustic parameter changes.Significance statementStudies of animal behaviour have traditionally relied on classification of behavioural patterns and analyses of discrete behavioural categories. Today, technologies such as animal-attached tags enable novel approaches, facilitating the use of quantitative metrics to characterize behaviour. In the field of acoustic communication, researchers typically classify vocalizations and examine usage of call categories. Through case studies of bottlenose dolphin social interactions, we present here a novel tag-based complementary approach. We used high-resolution tag data to parameterize dolphin sounds and motion, and we applied continuously sampled parameters to examine how individual dolphins responded to conspecifics’ signals and moved while producing sounds. Activity intensity of senders and receivers changed with specific call parameters, matching our predictions and illustrating the value of our approach to test communicative roles of acoustic parameter changes. Parametric approaches can complement traditional methods for animal behaviour and facilitate research replication.

Hidden Markov models for detection of Mysticetes vocalisations based on principal component analysis

ABSTRACT The economic relevance of Mysticetes has prompted marine ecologists and biologists to investigate this suborder of cetaceans. Mysticetes produce distinct vocal repertoires, which are recorded to analyse the behaviour of the species within its ecology. Passive acoustic monitoring (PAM) is a standard technique for tracking Mysticete movement and vocalisation. PAM collects enormous datasets over a long period, making it practically impossible to analyse with typical visual examination methods. Machine learning (ML) techniques such as hidden Markov models (HMMs) have made automatic recognition and analysis of extensive sound recordings possible. Nevertheless, the performance of ML tools is determined by the adopted feature extraction technique. Hence, this article introduces the method of principal component analysis (PCA) as a performance-efficient alternative feature extraction technique for detecting Mysticete vocalisations using HMM. Performance of the developed PCA-HMM detector is compared with state-of-the-art detectors using two different Mysticete vocalisations (Humpback whale songs and Bryde’s whale short pulses). In both species, results show that the PCA-HMM detector has the best performance and is more suitable for use in real-time application since it exhibits less computational time complexity.