Victor W. Young

Perception-based automatic classification of impulsive-source active sonar echoes

Impulsive-source active sonar systems are often plagued by false alarm echoes resulting from the presence of naturally occurring clutter objects in the environment. Sonar performance could be improved by a technique for discriminating between echoes from true targets and echoes from clutter. Motivated by anecdotal evidence that target echoes sound very different than clutter echoes when auditioned by a human operator, this paper describes the implementation of an automatic classifier for impulsive-source active sonar echoes that is based on perceptual signal features that have been previously identified in the musical acoustics literature as underlying timbre. Perceptual signal features found in this paper to be particularly useful to the problem of active sonar classification include: the centroid and peak value of the perceptual loudness function, as well as several features based on subband attack and decay times. This paper uses subsets of these perceptual signal features to train and test an automatic classifier capable of discriminating between target and clutter echoes with an equal error rate of roughly 10%; the area under the receiver operating characteristic curve corresponding to this classifier is found to be 0.975.

Performances of human listeners and an automatic aural classifier in discriminating between sonar target echoes and clutter.

Human listening tests were conducted to investigate if participants could distinguish between samples of target echoes and clutter obtained from a broadband active sonar experiment. For each echo, the listeners assigned a rating based on how confident they were that it was a target echo or clutter. The measure of performance was the area under the binormal receiver-operating-characteristic (ROC) curve, A(z). The mean performance was A(z)=0.95 ± 0.04 when signals were presented with their full available acoustic bandwidth of approximately 0-2 kHz. It was A(z)=0.77 ± 0.08 when the bandwidth was reduced to 0.5-2 kHz. The error bounds are stated as 95% confidence intervals. These results show that the listeners could definitely hear differences, but their performance was significantly degraded when the low-frequency signal information was removed. The performance of an automatic aural classifier was compared against this human-performance baseline. Results of statistical tests showed that it outperformed 2 of 13 listeners and 5 of 9 human listeners in the full-bandwidth and reduced-bandwidth tests, respectively, and performed similarly to the other listeners. Given its performance, the automatic aural classifier may prove beneficial to Navy sonar systems.

Study on the human ability to aurally discriminate between target echoes and environmental clutter in recordings of incoherent broadband sonar

Unacceptably high false‐alarm rates due to the inability to discriminate between target echoes and environmental clutter are an issue for existing low‐frequency active sonar systems operating in coastal environments. A research project at Defence R&D Canada—Atlantic is investigating the potential use of aural cues to tackle this challenge. One aspect of the project is to evaluate the human ability to aurally discriminate between target echoes and environmental clutter. The design and preliminary results from the study are presented here. Human subjects are presented with a series of sounds containing target echoes and clutter obtained from recordings of an incoherent broadband sonar experiment. The quantitative data collected in the study are the subjects’ decisions as to whether the echo heard was a target echo or clutter and their level of confidence associated with the decisions. Receiver‐operating characteristic (ROC) analysis is used to produce a statistical model of the subjects’ performance. The st...

Reconstruction and fusion of perceptual features for automatic classification of sonar echoes

The long detection ranges provided by low-frequency active sources present many advantages to localize and track underwater threats from safe distances. However, in littoral environments, echoes from naturally occurring features cause false alarms which degrade the overall system performance. The use of perceptual features derived from those used in the human auditory system (aural features), has been shown to allow discrimination between target and clutter echoes for both impulsive and coherent sources. The present work extends these findings by examining the effect of the sonar bandwidth on these kinds of features using a large data set gathered during an experiment on the Malta Plateau. Two separate bandwidths corresponding to those of two acoustic sources used during the experiment are considered independently. Using echoes from the two sources considered separately, it is possible to effectively reconstruct many of the features that would be derived with a full bandwidth signal. The results have implications for the use of fusion techniques where two separate sources are employed cooperatively and fused at the feature level to classify targets. Next, by using only those features that can effectively be reconstructed, it is possible to examine the full effect of bandwidth on the performance of a classification system which uses aural features. Results show that the system performance can be maintained with a narrower bandwidth if the center frequency is shifted downwards. Finally, fusion of the two sources at the decision-level is presented. Using the technique described, it is possible to achieve the same performance as a system using a single broadband source.

Application of musical timbre discrimination features to active sonar classification

In musical acoustics significant effort has been devoted to uncovering the physical basis of timbre perception. Most investigations into timbre rely on multidimensional scaling (MDS), in which different musical sounds are arranged as points in multidimensional space. The Euclidean distance between points corresponds to the perceptual distance between sounds and the multidimensional axes are linked to measurable properties of the sounds. MDS has identified numerous temporal and spectral features believed to be important to timbre perception. There is reason to believe that some of these features may have wider application in the disparate field of underwater acoustics, since anecdotal evidence suggests active sonar returns from metallic objects sound different than natural clutter returns when auralized by human operators. This is particularly encouraging since attempts to develop robust automatic classifiers capable of target‐clutter discrimination over a wide range of operational conditions have met with...

Performance of a superdirective line array in nonideal environments

Superdirective line arrays can provide high gains relative to their dimensions, whenever the inter‐element spacing is much less than half a wavelength. However, their performance can be degraded by system noise. System noise can result from limitations in electronic components, inter‐element mismatch in gain or phase, or from scatter from array components. Using state‐of‐the‐art electronics and digital signal processing one can drastically reduce the errors due to electronic noise as well as those due to gain and phase mismatch. Thus, acoustic scatter from the array components can determine the performance limit. Since theoretical developments typically assume plane waves incident on idealized point receivers, it is not all that surprising that array performance fails to meet theoretical expectations! This is especially true if the array is merely one component within a much larger system. Nonetheless, impressive gains can still be realized from superdirective arrays even with significant departures from ...

Experimental performance analysis of a superdirective line array

Superdirective line arrays can provide a significant array gain from a structure that is relatively small in terms of acoustic wavelengths. However, system imperfections, electronic noise and acoustic scatter from the array structure can degrade their performance. An acoustic calibration of a six‐element line array, 0.8 m in length, has been performed over the frequency range 1 to 4 kHz in order to investigate the performance of a real array. The data is used to identify the angular variation of the hydrophone outputs and the phase difference between hydrophone pairs. These angular responses are analyzed in terms of a modal series in order to quantify the variations and help identify the source of perturbations. The effects of imperfections are also investigated by synthesising superdirective arrays of order 1 to 5 and monitoring how the array gain varies for both deterministic signals and ambient acoustic noise. These results are compared with theoretical predictions. Further evidence of the variation in...

An experiment to investigate aural classification of coherent‐source active sonar data

Active sonar performance is degraded by the presence of environmental clutter objects, which lead to false alarm echoes. Earlier research suggests that perceptual signal features similar to those employed in the human auditory system can be used to automatically discriminate between impulsive‐source target and clutter echoes, thereby improving sonar performance by reducing the number of false alarms [V.W. Young et al., Can. Acoust. 34(3), 50–51 (2006)]. The present work attempts to extend these findings to the realm of coherent‐source active sonar echoes. To this end, an experiment was conducted on the Malta Plateau using a cardioid towed‐array and broadband FM sweeps running from 500 Hz up to 3.5 kHz. The data set consists of hundreds of pulse‐compressed echoes from several targets, shipwrecks, and clutter objects. It includes both monostatic and bistatic echoes, as well as echoes recorded at different aspect angles and at different propagation ranges between roughly 5 km and 30 km. In this paper the exp...

Active sonar classification using perceptual signal features from musical acoustics

Because of the relatively low frequencies at which they operate, navy active sonars are often plagued by false‐alarm returns resulting from geological structures. In the lexicon of sonar operators these false returns are referred to as geoclutter or simply clutter. Despite mounting evidence that human operators can aurally discriminate target returns from clutter, attempts to develop robust automatic classification algorithms have, as yet, met with limited success. This paper investigates the possibility of improving the performance of automatic classifiers by exploiting the signal processing employed in the human auditory system. This amounts to replacing the statistical signal features used by conventional automatic classifiers with perceptual signal features that reflect the way a human listener would perceive a given return. Drawing an analogy between active sonar returns and percussive musical timbre, this paper considers perceptual signal features—like loudness centroid, sub‐band attack/decay, and s...

Practical application of a tri‐axial intensity array

Sound intensity is a vector quantity representing the magnitude and direction of propagating energy within an acoustic field. In an underwater environment, a single omni‐directional hydrophone can be used to measure instantaneous acoustic pressure and a finite difference approximation applied to the pressure signals from a pair of such hydrophones can be used to calculate particle velocity in a single direction. Because the time average of the product of instantaneous pressure and particle velocity is intensity, a pair of hydrophones is all that is required to measure a single component of the intensity vector. The complete three‐dimensional intensity vector can be calculated using three orthogonal pairs of hydrophones. To evaluate this concept a tri‐axial array consisting of three orthogonal pairs of omni‐directional hydrophones has been developed and tested on both calibrated sources at a laboratory facility and sources of opportunity during sea trails in littoral waters. The use of this array to calcul...