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The auditory filter shape of delphinid odontocetes was previously considered to be typically mammalian constant-quality in which filter bandwidths increase proportionally with frequency. Recent studies with porpoises demonstrate constant-bandwidth portions of the auditory filter. The critical ratios for a bottlenose dolphin were measured between 40 and 120 kHz by behaviorally determining the subject's ability to detect pure tones in the presence of white noise. Critical ratios as a function of frequency were constant, indicating the auditory filter acts as a constant-bandwidth system in this frequency range. Re-analysis of past studies supports these findings, and suggests the delphinid auditory system is best characterized as a constant-Q system below 40 kHz and a constant-bandwidth-like system between 40 kHz and 120 kHz before returning to a constant Q pattern at the highest frequencies.

The hearing sensitivity of an Atlantic bottlenose dolphin (Tursiops truncatus) to both pure tones and broadband signals simulating echoes from a 7.62-cm water-filled sphere was measured. Pure tones with frequencies between 40 and 140 kHz in increments of 20 kHz were measured along with broadband thresholds using a stimulus with a center frequency of 97.3 kHz and 88.2 kHz. The pure-tone thresholds were compared with the broadband thresholds by converting the pure-tone threshold intensity to energy flux density. The results indicated that dolphins can detect broadband signals slightly better than a pure-tone signal. The broadband results suggest that an echolocating bottlenose dolphin should be able to detect a 7.62-cm diameter water-filled sphere out to a range of 178 m in a quiet environment.

High-frequency auditory filter shapes of an Atlantic bottlenose dolphin (Tursiops truncatus) were measured using a notched noise masking source centered on pure tone signals at frequencies of 40, 60, 80 and 100 kHz. A dolphin was trained to swim into a hoop station facing the noise/signal transducer located at a distance of 2 m. The dolphin's masked threshold was determined using an up-down staircase method as the width of the notched noise was randomly varied from 0, 0.2, 04, 0.6, and 0.8 times the test tone frequency. The masked threshold decreased as the width of the notched increased and less noise fell within the auditory filter associated with the test tone. The auditory filter shapes were approximated by fitting a roex (p,r(r)) function to the masked threshold results. A constant-Q value of 8.4 modeled the results within the frequency range of 40 to 100 kHz relatively well. However, between 60 and 100 kHz, the 3 dB bandwidth was relatively similar between 9.5 and 10 kHz, indicating a constant-bandwidth system in this frequency range The mean equivalent rectangular bandwidth calculated from the filter shape was approximately 16.0%, 17.0%, 13.6% and 11.3% of the tone frequencies of 40, 60, 80, and 100 kHz.

The focus of this study was to investigate how dolphins use acoustic features in returning echolocation signals to discriminate among objects. An echolocating dolphin performed a match-to-sample task with objects that varied in size, shape, material, and texture. After the task was completed, the features of the object echoes were measured (e.g., target strength, peak frequency). The dolphin’s error patterns were examined in conjunction with the between-object variation in acoustic features to identify the acoustic features that the dolphin used to discriminate among the objects. The present study explored two hypotheses regarding the way dolphins use acoustic information in echoes: (1) use of a single feature, or (2) use of a linear combination of multiple features. The results suggested that dolphins do not use a single feature across all object sets or a linear combination of six echo features. Five features appeared to be important to the dolphin on four or more sets: the echo spectrum shape, the pattern of changes in target strength and number of highlights as a function of object orientation, and peak and center frequency. These data suggest that dolphins use multiple features and integrate information across echoes from a range of object orientations.

Acoustic detection and classification of buried mines presents a challenging and, as of yet, unsolved object recognition problem. Techniques for detecting and classifying mine-like targets in backscatter images created with broadband sonars are starting to yield promising results. These images represent energy vs. time mappings of target echoes; however, further information on potential targets can be extracted from the spectral and temporal content of these broadband signals. An approach to classifying the time-frequency characteristics of target echoes measured with a 3D, 5-23 kHz, forward/aft sweeping, buried object imaging system is described. Matched spatial filters are applied to backscatter images representing horizontal seafloor slices. Detection outputs are used to identify beams and ranges for candidate targets. Time- spectral features are then extracted from the appropriate time-series component, and input to an artificial neural network trained to identify mine-like objects. Fusion of image and signal classification algorithms applied to these data is expected to result in rapid identification of buried targets and a reduction in false alarm rates.

Whales and dolphins have evolved in a sensory world very unlike our own. Although one can guess what the perceptions of a cetacean might be, it is impossible to “get inside a dolphins head” and experience what it must be like to hear sounds over 100kHz and discern fine details of the environment, conspecifics, and prey via echolocation. It is possible, however, using psychophysical techniques first developed for human measurements, to characterize some properties of cetacean perception and accurately measure the acoustic sensations that are experienced by whales and dolphins.

Principal component analysis (PCA) in the wavelet domain provides powerful features for underwater object recognition applications. The multiresolution analysis of the Morlet wavelet transform (MWT) is used to pre-process echo returns from targets ensonified by biologically motivated broadband signal. PCA is then used to compress and denoise the resulting time-scale signal representation for presentation to a hierarchical neural network for object classification. Wavelet/PCA features combined with multi-aspect data fusion and neural networks have resulted in impressive underwater object recognition performance using backscatter data generated by simulate dolphin echolocation clicks and bat- like linear frequency modulated upsweeps. For example, wavelet/PCA features extracted from LFM echo returns have resulted in correct classification rates of 98.6 percent over a six target suite, which includes two mine simulators and four clutter objects. For the same data, ROC analysis of the two-class mine-like versus non-mine-like problem resulted in a probability of detection of 0.981 and a probability of false alarm of 0.032 at the 'optimal' operating point. The wavelet/PCA feature extraction algorithm is currently being implemented in VLSI for use in small, unmanned underwater vehicles designed for mine- hunting operations in shallow water environments.© (1999) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Signal features based on multiresolution short-time Fourier transforms (STFT) and the Morlet wavelet transform (MWT) have been developed to classify echo returns from targets ensonified by simulated dolphin echolocation clicks. Spectrogram features are obtained at different scales of resolution using analysis windows of different sizes. A method of compressing the highly redundant time-scale representations provided by the MWT has been developed based on multiscale edge analysis (MSEA) of wavelet local maxima. Neural networks are used to evaluate the efficacy of the various feature sets for target recognition. Hierarchical neural networks are used to combine different feature sets for improved classification performance.

The Atlantic bottlenose dolphin has a sonar detection range of 72 m for a 2.54‐cm solid sphere and 113 m for a 7.63‐cm water‐filled sphere in a noise‐limited environment. However, there are many natural environments in which the ambient noise is low and will not mask a dolphin auditory system. The dolphin’s detection range for the same 2.54‐ or 7.62‐cm sphere in a nonmasking environment should be considerably longer than 72 and 113 m, respectively. In order to perform a sonar experiment in an environment where the ambient noise is low, a pool must be extremely large, or an outdoor facility with low ambient noise conditions would need to be available. Another way of approaching this problem is to perform a passive listening experiment in which the signal presented to a dolphin simulates an echo from a specific target. We performed a listening experiment using both pure tones and simulated broadband echo from a 7.62‐cm water‐filled sphere. The detection threshold for narrow‐band tonal signals (audiogram) is compared with the detection threshold for a broadband echo signal in order to determine the relationship between an audiogram and sonar detection. The results obtained with the broadband echo also represent the sonar detection range of a dolphin in a nonmasking environment. [Work suported by ONR.]

Frequency selectivity capabilities of an Atlantic bottlenose dolphin were examined by calculating critical ratios from masked hearing data. Absolute sensitivity to pure tones from 40–140 kHz was measured as a base line, and masked sensitivity was determined for the same signals masked by three levels of white noise (52, 42, and 32 dB re:1 μ Pa2/Hz). Absolute and masked sensitivity were essentially constant between 40 and 120 kHz at each of the masking conditions. Sensitivity decreased approximately 100 dB per octave between 120 and 140 kHz. Critical ratios averaged across noise levels were constant between 40 and 120 kHz, averaging 26 dB. The critical ratio at 140 kHz was 42 dB. This flat trend in critical ratios between 40–120 kHz does not agree with the constant‐Q filter‐bank models used to account for earlier critical ratio and bandwidth measurements for the species [W. W. L. Au and P. W. B. Moore, J. Acoust. Soc. Am. 88, 1635–1638 (1990)], but comparison to other cetacean masked hearing work [Johnson et al., J. Acoust. Soc. Am. 85, 2651–2654 (1989)] indicates that the trend has been recorded before, but not explicitly reported.