Marie A. Roch

Classification of Risso’s and Pacific white-sided dolphins using spectral properties of echolocation clicks

The spectral and temporal properties of echolocation clicks and the use of clicks for species classification are investigated for five species of free-ranging dolphins found offshore of southern California: short-beaked common (Delphinus delphis), long-beaked common (D. capensis), Rissos (Grampus griseus), Pacific white-sided (Lagenorhynchus obliquidens), and bottlenose (Tursiops truncatus) dolphins. Spectral properties are compared among the five species and unique spectral peak and notch patterns are described for two species. The spectral peak mean values from Pacific white-sided dolphin clicks are 22.2, 26.6, 33.7, and 37.3 kHz and from Rissos dolphins are 22.4, 25.5, 30.5, and 38.8 kHz. The spectral notch mean values from Pacific white-sided dolphin clicks are 19.0, 24.5, and 29.7 kHz and from Rissos dolphins are 19.6, 27.7, and 35.9 kHz. Analysis of variance analyses indicate that spectral peaks and notches within the frequency band 24-35 kHz are distinct between the two species and exhibit low variation within each species. Post hoc tests divide Pacific white-sided dolphin recordings into two distinct subsets containing different click types, which are hypothesized to represent the different populations that occur within the region. Bottlenose and common dolphin clicks do not show consistent patterns of spectral peaks or notches within the frequency band examined (1-100 kHz).

Acoustic sequences in non-human animals: a tutorial review and prospectus

Animal acoustic communication often takes the form of complex sequences, made up of multiple distinct acoustic units. Apart from the well‐known example of birdsong, other animals such as insects, amphibians, and mammals (including bats, rodents, primates, and cetaceans) also generate complex acoustic sequences. Occasionally, such as with birdsong, the adaptive role of these sequences seems clear (e.g. mate attraction and territorial defence). More often however, researchers have only begun to characterise – let alone understand – the significance and meaning of acoustic sequences. Hypotheses abound, but there is little agreement as to how sequences should be defined and analysed. Our review aims to outline suitable methods for testing these hypotheses, and to describe the major limitations to our current and near‐future knowledge on questions of acoustic sequences. This review and prospectus is the result of a collaborative effort between 43 scientists from the fields of animal behaviour, ecology and evolution, signal processing, machine learning, quantitative linguistics, and information theory, who gathered for a 2013 workshop entitled, ‘Analysing vocal sequences in animals’. Our goal is to present not just a review of the state of the art, but to propose a methodological framework that summarises what we suggest are the best practices for research in this field, across taxa and across disciplines. We also provide a tutorial‐style introduction to some of the most promising algorithmic approaches for analysing sequences. We divide our review into three sections: identifying the distinct units of an acoustic sequence, describing the different ways that information can be contained within a sequence, and analysing the structure of that sequence. Each of these sections is further subdivided to address the key questions and approaches in that area. We propose a uniform, systematic, and comprehensive approach to studying sequences, with the goal of clarifying research terms used in different fields, and facilitating collaboration and comparative studies. Allowing greater interdisciplinary collaboration will facilitate the investigation of many important questions in the evolution of communication and sociality.

Gaussian mixture model classification of odontocetes in the Southern California Bight and the Gulf of California

A method for the automatic classification of free-ranging delphinid vocalizations is presented. The vocalizations of short-beaked and long-beaked common (Delphinus delphis and Delphinus capensis), Pacific white-sided (Lagenorhynchus obliquidens), and bottlenose (Tursiops truncatus) dolphins were recorded in a pelagic environment of the Southern California Bight and the Gulf of California over a period of 4 years. Cepstral feature vectors are extracted from call data which contain simultaneous overlapping whistles, burst-pulses, and clicks from a single species. These features are grouped into multisecond segments. A portion of the data is used to train Gaussian mixture models of varying orders for each species. The remaining call data are used to test the performance of the models. Species are predicted based upon probabilistic measures of model similarity with test segment groups having durations between 1 and 25 s. For this data set, 256 mixture Gaussian mixture models and segments of at least 10 s of call data resulted in the best classification results. The classifier predicts the species of groups with 67%-75% accuracy depending upon the partitioning of the training and test data.

Classification of echolocation clicks from odontocetes in the Southern California Bight

This study presents a system for classifying echolocation clicks of six species of odontocetes in the Southern California Bight: Visually confirmed bottlenose dolphins, short- and long-beaked common dolphins, Pacific white-sided dolphins, Rissos dolphins, and presumed Cuviers beaked whales. Echolocation clicks are represented by cepstral feature vectors that are classified by Gaussian mixture models. A randomized cross-validation experiment is designed to provide conditions similar to those found in a field-deployed system. To prevent matched conditions from inappropriately lowering the error rate, echolocation clicks associated with a single sighting are never split across the training and test data. Sightings are randomly permuted before assignment to folds in the experiment. This allows different combinations of the training and test data to be used while keeping data from each sighting entirely in the training or test set. The system achieves a mean error rate of 22% across 100 randomized three-fold cross-validation experiments. Four of the six species had mean error rates lower than the overall mean, with the presumed Cuviers beaked whale clicks showing the best performance (<2% error rate). Long-beaked common and bottlenose dolphins proved the most difficult to classify, with mean error rates of 53% and 68%, respectively.

Species-specific beaked whale echolocation signals

Beaked whale echolocation signals are mostly frequency-modulated (FM) upsweep pulses and appear to be species specific. Evolutionary processes of niche separation may have driven differentiation of beaked whale signals used for spatial orientation and foraging. FM pulses of eight species of beaked whales were identified, as well as five distinct pulse types of unknown species, but presumed to be from beaked whales. Current evidence suggests these five distinct but unidentified FM pulse types are also species-specific and are each produced by a separate species. There may be a relationship between adult body length and center frequency with smaller whales producing higher frequency signals. This could be due to anatomical and physiological restraints or it could be an evolutionary adaption for detection of smaller prey for smaller whales with higher resolution using higher frequencies. The disadvantage of higher frequencies is a shorter detection range. Whales echolocating with the highest frequencies, or broadband, likely lower source level signals also use a higher repetition rate, which might compensate for the shorter detection range. Habitat modeling with acoustic detections should give further insights into how niches and prey may have shaped species-specific FM pulse types.

Automated extraction of odontocete whistle contours

Many odontocetes produce frequency modulated tonal calls known as whistles. The ability to automatically determine time × frequency tracks corresponding to these vocalizations has numerous applications including species description, identification, and density estimation. This work develops and compares two algorithms on a common corpus of nearly one hour of data collected in the Southern California Bight and at Palmyra Atoll. The corpus contains over 3000 whistles from bottlenose dolphins, long- and short-beaked common dolphins, spinner dolphins, and melon-headed whales that have been annotated by a human, and released to the Moby Sound archive. Both algorithms use a common signal processing front end to determine time × frequency peaks from a spectrogram. In the first method, a particle filter performs Bayesian filtering, estimating the contour from the noisy spectral peaks. The second method uses an adaptive polynomial prediction to connect peaks into a graph, merging graphs when they cross. Whistle contours are extracted from graphs using information from both sides of crossings. The particle filter was able to retrieve 71.5% (recall) of the human annotated tonals with 60.8% of the detections being valid (precision). The graph algorithms recall rate was 80.0% with a precision of 76.9%.

Spatio-temporal patterns of beaked whale echolocation signals in the North Pacific.

At least ten species of beaked whales inhabit the North Pacific, but little is known about their abundance, ecology, and behavior, as they are elusive and difficult to distinguish visually at sea. Six of these species produce known species-specific frequency modulated (FM) echolocation pulses: Baird’s, Blainville’s, Cuvier’s, Deraniyagala’s, Longman’s, and Stejneger’s beaked whales. Additionally, one described FM pulse (BWC) from Cross Seamount, Hawai’i, and three unknown FM pulse types (BW40, BW43, BW70) have been identified from almost 11 cumulative years of autonomous recordings at 24 sites throughout the North Pacific. Most sites had a dominant FM pulse type with other types being either absent or limited. There was not a strong seasonal influence on the occurrence of these signals at any site, but longer time series may reveal smaller, consistent fluctuations. Only the species producing BWC signals, detected throughout the Pacific Islands region, consistently showed a diel cycle with nocturnal foraging. By comparing stranding and sighting information with acoustic findings, we hypothesize that BWC signals are produced by ginkgo-toothed beaked whales. BW43 signal encounters were restricted to Southern California and may be produced by Perrin’s beaked whale, known only from Californian waters. BW70 signals were detected in the southern Gulf of California, which is prime habitat for Pygmy beaked whales. Hubb’s beaked whale may have produced the BW40 signals encountered off central and southern California; however, these signals were also recorded off Pearl and Hermes Reef and Wake Atoll, which are well south of their known range.

A generalized power-law detection algorithm for humpback whale vocalizations

Conventional detection of humpback vocalizations is often based on frequency summation of band-limited spectrograms under the assumption that energy (square of the Fourier amplitude) is the appropriate metric. Power-law detectors allow for a higher power of the Fourier amplitude, appropriate when the signal occupies a limited but unknown subset of these frequencies. Shipping noise is non-stationary and colored and problematic for many marine mammal detection algorithms. Modifications to the standard power-law form are introduced to minimize the effects of this noise. These same modifications also allow for a fixed detection threshold, applicable to broadly varying ocean acoustic environments. The detection algorithm is general enough to detect all types of humpback vocalizations. Tests presented in this paper show this algorithm matches human detection performance with an acceptably small probability of false alarms (P(FA) < 6%) for even the noisiest environments. The detector outperforms energy detection techniques, providing a probability of detection P(D) = 95% for P(FA) < 5% for three acoustic deployments, compared to P(FA) > 40% for two energy-based techniques. The generalized power-law detector also can be used for basic parameter estimation and can be adapted for other types of transient sounds.

Discriminating features of echolocation clicks of melon-headed whales (Peponocephala electra), bottlenose dolphins (Tursiops truncatus), and Gray's spinner dolphins (Stenella longirostris longirostris)

Spectral parameters were used to discriminate between echolocation clicks produced by three dolphin species at Palmyra Atoll: melon-headed whales (Peponocephala electra), bottlenose dolphins (Tursiops truncatus) and Grays spinner dolphins (Stenella longirostris longirostris). Single species acoustic behavior during daytime observations was recorded with a towed hydrophone array sampling at 192 and 480 kHz. Additionally, an autonomous, bottom moored High-frequency Acoustic Recording Package (HARP) collected acoustic data with a sampling rate of 200 kHz. Melon-headed whale echolocation clicks had the lowest peak and center frequencies, spinner dolphins had the highest frequencies and bottlenose dolphins were nested in between these two species. Frequency differences were significant. Temporal parameters were not well suited for classification. Feature differences were enhanced by reducing variability within a set of single clicks by calculating mean spectra for groups of clicks. Median peak frequencies of averaged clicks (group size 50) of melon-headed whales ranged between 24.4 and 29.7 kHz, of bottlenose dolphins between 26.7 and 36.7 kHz, and of spinner dolphins between 33.8 and 36.0 kHz. Discriminant function analysis showed the ability to correctly discriminate between 93% of melon-headed whales, 75% of spinner dolphins and 54% of bottlenose dolphins.

TRITON software package: Analyzing large passive acoustic monitoring data sets using MATLAB.

As consumer electronic technology continues to advance with lower power, higher speed, larger data capacity, and lower costs, the ability to collect large acoustic data sets becomes more practical. However, working with these large data sets can be challenging and requires different approaches to managing and analyzing the data than traditionally have been used with small data sets. TRITON is a software package developed for working with long‐term, wide‐band, multi‐terabyte, acoustic data sets. One of TRITON’s primary functions is to make long‐term spectrograms from a large group of small (1 Gbyte) sequential data files. These long‐term spectral averages provide a means of quickly evaluating long durations (hours) of wide‐band data for sounds such as animal calling bouts and permit the user to zoom in to traditional spectral/time domain displays for finer scale analysis. TRITON has a graphical user interface and is based in MATLAB, which allows for easy modifications for specific data processing and analy...