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Bottlenose dolphins (Tursiops truncatus) use short, wideband pulses for echolocation. Individual waveforms have high-range resolution capability but are relatively insensitive to range rate. Signal-to-noise ratio (SNR) is not greatly improved by pulse compression because each waveform has small time-bandwidth product. The dolphin, however, often uses many pulses to interrogate a target, and could use multipulse processing to combine the resulting echoes. Multipulse processing could mitigate the small SNR improvement from pulse compression, and could greatly improve range-rate estimation, moving target indication, range tracking, and acoustic imaging. All these hypothetical capabilities depend upon the animal's ability to combine multiple echoes for detection and/or estimation. An experiment to test multiecho processing in a dolphin measured detection of a stationary target when the number N of available target echoes was increased, using synthetic echoes. The SNR required for detection decreased as the number of available echoes increased, as expected for multiecho processing. A receiver that sums binary-quantized data samples from multiple echoes closely models the N dependence of the SNR required by the dolphin. Such a receiver has distribution-tolerant (nonparametric) properties that make it robust in environments with nonstationary and/or non-Gaussian noise, such as the pulses created by snapping shrimp.

The relationships between acoustic features of target echoes and the cognitive representations of the target formed by an echolocating dolphin will influence the ease with which the dolphin can recognize a target. A blindfolded Atlantic bottlenose dolphin (Tursiops truncatus) learned to match aspect-dependent three-dimensional targets (such as a cube) at haphazard orientations, although with some difficulty. This task may have been difficult because aspect-dependent targets produce different echoes at different orientations, which required the dolphin to have some capability for object constancy across changes in echo characteristics. Significant target-related differences in echo amplitude, rms bandwidth, and distributions of interhighlight intervals were observed among echoes collected when the dolphin was performing the task. Targets could be classified using a combination of energy flux density and rms bandwidth by a linear discriminant analysis and a nearest centroid classifier. Neither statistical model could classify targets without amplitude information, but the highest accuracy required spectral information as well. This suggests that the dolphin recognized the targets using a multidimensional representation containing amplitude and spectral information and that dolphins can form stable representations of targets regardless of orientation based on varying sensory properties.

Bottlenose dolphins (Tursiops truncatus) detect and discriminate underwater objects by interrogating the environment with their native echolocation capabilities. Study of dolphins' ability to detect complex (multihighlight) signals in noise suggest echolocation object detection using an approximate 265-micros energy integration time window sensitive to the echo region of highest energy or containing the highlight with highest energy. Backscatter from many real objects contains multiple highlights, distributed over multiple integration windows and with varying amplitude relationships. This study used synthetic echoes with complex highlight structures to test whether high-amplitude initial highlights would interfere with discrimination of low-amplitude trailing highlights. A dolphin was trained to discriminate two-highlight synthetic echoes using differences in the center frequencies of the second highlights. The energy ratio (delta dB) and the timing relationship (delta T) between the first and second highlights were manipulated. An iso-sensitivity function was derived using a factorial design testing delta dB at -10, -15, -20, and -25 dB and delta T at 10, 20, 40, and 80 micros. The results suggest that the animal processed multiple echo highlights as separable analyzable features in the discrimination task, perhaps perceived through differences in spectral rippling across the duration of the echoes.

Steller sea lion (Eumetopias jubatus) mothers and pups establish and maintain contact with individually distinctive vocalizations. Our objective was to develop a robust neural network to classify females based on their mother-pup contact calls. We catalogued 573 contact calls from 25 females in 1998 and 1323 calls from 46 females in 1999. From this database, a subset of 26 females with sufficient samples of calls was selected for further study. Each female was identified visually by marking patterns, which provided the verification for acoustic identification. Average logarithmic spectra were extracted for each call, and standardized training and generalization datasets created for the neural network classifier. A family of backpropagation networks was generated to assess relative contribution of spectral input bandwidth, frequency resolution, and network architectural variables to classification accuracy. The network with best overall generalization accuracy (71%) used an input representation of 0-3 kHz of bandwidth at 10.77 Hz/bin frequency resolution, and a 2:1 hidden:output layer neural ratio. The network was analyzed to reveal which portions of the call spectra were most influential for identification of each female. Acoustical identification of distinctive female acoustic signatures has several potentially important conservation applications for this endangered species, such as rapid survey of females present on a rookery.

Dolphins demonstrate an adaptive control over echolocation click production, but little is known of the manner or degree with which control is exercised. Echolocation clicks (N approximately 30,000) were collected from an Atlantic bottlenose dolphin (Tursiops truncatus) performing object discrimination tasks in order to investigate differential click production. Seven categories of clicks were identified using the spectral conformation and relative position of -3 and -10 dB peaks. A counterpropagation network utilizing 16 inputs, 50 hidden units, and 8 output units was trained to classify clicks using the same spectral variables. The network classified novel clicks with 92% success. Additional echolocation clicks (N24,000) from two other dolphins were submitted to the network for classification. Classified echolocation clicks were analyzed for animal specific differences, changes in predominant click type within click trains, and task-related specificity. Differences in animal and task performance may influence click type and click train length.

This study demonstrated the ability of a false killer whale (Pseudorca crassidens) to discriminate between two targets and investigated the parameters of the whale's emitted signals for changes related to test conditions. Target detection performance comparable to the bottlenose dolphin's (Tursiops truncatus) has previously been reported for echolocating false killer whales. No other echolocation capabilities have been reported. A false killer whale, naive to conditioned echolocation tasks, was initially trained to detect a cylinder in a "go/no-go" procedure over ranges of 3 to 8 m. The transition from a detection task to a discrimination task was readily achieved by introducing a spherical comparison target. Finally, the cylinder was successfully compared to spheres of two different sizes and target strengths. Multivariate analyses were used to evaluate the parameters of emitted signals. Duncan's multiple range tests showed significant decreases (df = 185, p less than 0.05) in both source level and bandwidth in the transition from detection to discrimination. Analysis of variance revealed a significant decrease in the number of clicks over test conditions [F(5.26) = 5.23, p less than 0.0001]. These data suggest that the whale relied on cues relevant to target shape as well as target strength, that changes in source level and bandwidth were task-related, that the decrease in clicks was associated with learning experience, and that Pseudorca's ability to discriminate shapes using echolocation may be comparable to that of Tursiops truncatus.

Steller sea lions (Eumetopias jubatus) breed and rear their young in coastal rookeries dispersed along the northern tier of the North Pacific. Population densities are high and no allomaternal behavior occurs, providing strong selection pressure for mother–pup recognition processes. Mothers and pups establish and maintain contact with individually distinctive vocalizations. Our objective is to understand the acoustic features that serve to identify individual females, and develop a ruggedized computer system to perform acoustic recognition of females in the field. We have cataloged almost 2000 contact calls from 46 females in 1998 and 25 in 1999. Each female is visually identified by marking patterns, which provides the ground truth for acoustic identification. Acoustic properties of the calls were measured and presented to several statistical classifiers. Representations of the calls had to be robust with respect to acoustical variability introduced by motivational changes as the mother and pup regained proximity. The calls, classifiers, and results of generalization tests will be described, and a concept for the field system will be discussed. [Research supported by ONR Research Opportunities for Program Officers award N00014‐00‐1‐0114 to R. H. DeFran, Cetacean Behavior Laboratory, SDSU.]

Many species of baleen whales reside or migrate annually through California waters. During these times some species of vocalizing whales can be detected, identified, and tracked with SOSUS arrays. Historical research by W. Cummings documented strong seasonal changes in narrow‐band ambient sound associated with then unidentified sounds. Contemporary research shows these sounds to be blue and fin whale vocalizations. The current study compares historical and contemporary narrow‐band ambient measurements with respect to mysticete residency and movement through southern California waters. Seasonal peaks in ambient noise agree with earlier reports, but increased anthropogenic sound reduced the magnitude of the effect. Potential sources of changes in ambient noise and their effects on the whales are discussed. [Research supported by the Strategic Environmental Research and Development Program, Project CS‐1082.]

The hypothesis that echolocating dolphins best receive acoustic signals over the pan bones of the lower jaw is widely accepted. Studies in echolocation and hearing have assumed that those areas serve as the dolphin’s peripheral hearing system. The research that established that model, however, does not exclude other potential sound reception sites and suggests that additional areas of the head may be acoustically sensitive and perhaps frequency dependent. Using jawphones, relative hearing thresholds for representative frequencies (10, 30, 60, and 90 kHz) were behaviorally measured at over 40 sites on a dolphin’s head. Iso‐sensitivity curves were constructed and projected onto the image of a dolphin’s head based on these measurements. The results suggest sensitivity to high frequency along the lower jaw with greater sensitivity forward of the pan bone area, sensitivity to low frequency around the external auditory meatus, and an acoustic asymmetry with greater sensitivity favoring the right side of the head. These results may correlate to underlying anatomical features and suggest a more complex peripheral hearing system than has been previously assumed.

Modeling cetacean hearing is an important step in advancing models of echolocation and in predicting the potential impact of oceanic noise upon cetaceans. Modeling the response of cetacean ears to acoustic input can be a difficult task and the performance of such models is often sub‐optimal. Evolutionary programming was employed as a systematic method of optimizing the response of a bank of pseudo‐Gaussian filter shapes to known audiometric functions in order to model the resonant systems of the basilar membrane. The response of the filter banks was tested against normalized audiograms of the bottlenose dolphin (Tursiops truncatus) and the predicted frequency dependent sensitivity of the Humpback whale (Megaptera novaeangliae). Filter banks were created that demonstrated comparable frequency responses to tonal stimuli across the (predicted) range of hearing in both species. The potential for incorporating these ear models into models of echolocation and their use in predicting the sensitivity of baleen wh...

Baleen whales live over extensive home range and time scales. Study of how these animals use their vocalizations for communication requires massive data sampling over long periods. This paper describes a system for automating the sampling and analysis of baleen whale calls. Many species produce stable, homogeneous call structures which lend themselves to automated species identification. We have benchmarked a series of bioacoustical call identification algorithms against a set of blue and fin whale calls while systematically manipulating the signal‐to‐noise ratio. Blue (Balaenoptera musculus) and fin (B. physalus) whale calls are very stereotypical. Blue whale ‘‘A’’ and ‘‘B’’ calls have fundamental frequencies of approximately 17 Hz, narrow bandwidth, well‐defined harmonic structure, and typical duration of 15–25 s. Fin whale ‘‘pulses’’ have fundamental frequencies of approximately 17 Hz, but are broadband in nature and short (approximately 1‐s) duration. The results demonstrated a typical tradeoff of speed versus accuracy. The best algorithm was inserted into an underwater sound recording system and its signal‐detection theoretic performance was quantified. Results will be discussed with respect to technological, ecological, and conservation aspects of baleen whale bioacoustics. [Project CS‐1082 of the Strategic Environmental Research and Development Program.]

The biological sonar system of bottlenose dolphins is adapted for cluttered, high noise, and extremely reverberant shallow‐water environments such as bays, estuaries, and near‐shore waterways. Echolocation behavior and signals are plastic, modified in‐stride during encounters with novel targets and in novel surroundings. Moreover, biosonar target discrimination performance provides an existence proof for biological sonar‐based recognition of various kinds of targets. For the dolphin this is accomplished in part by auditory neural computations that are not yet fully understood. A model of adaptive target classification processes was developed using heuristics based on dolphin psychophysics and echolocation behavior. Echoes were recorded while a dolphin ensonified several aspect‐dependent targets during a target discrimination task. A computational model of dolphin ear filters was adapted to include filter gain recursively scaled to acoustical characteristics of local ambient noise and reverberation. Stocha...

Contemporary anti‐invasion mines have aspect‐dependent shapes, making discrimination of mines from nonthreat objects, and classification of mines, a difficult task for human mine countermeasures personnel. Shallow‐water (SW) noise and bottom reverberation substantially degrade the acoustic structure of mine echoes. Traditional MCM target strength and FFT classifiers are not effective under these conditions. The testing of a novel neural network classifier to solve the task of classifying mines in the SW acoustic environment was begun. Three mine types were ensonified at 1 deg rotations using synthetic dolphin clicks. A learning vector quantization network was trained to classify the mines using the Choi–Williams joint time‐frequency distributions of echoes. A training set of 36 echoes per mine (5, 15, 25,..., 355 deg) was created, with the remaining echoes (0, 10, 20,..., 350 deg) reserved for generalization testing. The network correctly classified the mines at novel orientations with 85% accuracy. Perfo...

Humpback whales (Megaptera novaeangliae) migrate annually between high‐latitude summer feeding waters and low‐latitude winter mating waters. During migration and in winter waters, adult [male] humpbacks produce long complex songs. Songs gradually change during each singing season, and singers incorporate changes as they occur. Classification of song units using neural networks and cepstral cross correlation reveals that variability in song characteristics within region is lower than variability between regions. These regional differences in song characteristics can be considered to be dialects that are maintained through cultural transmission of regional variants. Thus differences in songs from different regions may be an index of reproductive isolation. Cladistic patterns of geographic and temporal variation in songs support a model of limited migratory exchange among widely separate wintering regions. [Work supported by UARC, SPREP, WDCS, PDCT.]