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Automated extraction of odontocete whistle contours
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Image of FIG. 1.
FIG. 1.

(Color online) Particle filter performance in whistle discovery as shown with a spectrogram. Approximate boundary of an odontocete whistle is marked by the solid lines. Detected peaks of whistle are shown as squares. Particles in each time step are shown as ×’s, and the center of mass of the particles is depicted as circles. These are the effective measures of the whistle contour in each time step. Tracking continues even through time frames without nearby whistle peaks allowing whistle contour detection to resume once peaks are detected again. Locations where the whistle contour detection is resumed are denoted with darker circles.

Image of FIG. 2.
FIG. 2.

(Color online) Graph extension. Dashed curves depict an active graph. A peak is depicted by an asterisk and ordinary least squares regression curves are fit along the closest 25 ms of paths near the peak as indicated by the change in shade and dash pattern. Peaks that are within 1 kHz of the path predicted by the polynomial fits will be added to the graph.

Image of FIG. 3.
FIG. 3.

Whistle detection algorithm performance amid the interference of odontocete echolocation clicks. The uppermost panel shows a spectrogram of 5 s of long-beaked common dolphin call data (analysis bandwidth 125 Hz) with relative dBs of signal to noise ratio encoded by gray levels. The second panel shows the whistles detected by the particle filter algorithm. The last two panels show the whistle graphs and extracted whistles as detected by the graph search algorithm.

Image of FIG. 4.
FIG. 4.

Graph disambiguation. When deciding whether the incoming arc should be joined with the outgoing arc or , polynomials are estimated for all three arcs. The sum of the squared prediction errors of pairs of incoming and outgoing edges [Eq. (9)] is used to determine which pairs should be joined. In this example, is joined to .

Image of FIG. 5.
FIG. 5.

(Color online) Common subpaths. The graph for these common dolphin whistles shows a dashed segment that is shared between two whistles. The intersection nodes are characterized by having multiple inputs on one side and multiple outputs on the other, joined by a single segment. When this occurs, the disambiguation algorithm permits the segment to be used in more than one whistle, permitting both whistles in the figure above to be recognized.

Image of FIG. 6.
FIG. 6.

(Color online) Metrics used to characterize detections. The Venn diagram on the left shows the overlap between the detected tonals and ground truth data. Recall computes the percentage of correct detections relative to the ground truth while precision is the percentage of detections that were correct. The exaggerated caricatures of a call and associated detections on the right illustrate the quality metrics. Average deviation is the mean frequency deviation between the tonal call and detection(s). As systems may detect a call in multiple pieces, or fragments, the number of fragments per call is recorded. Coverage is an indication of the percentage of the tonal that was detected and in this case would be . Call and detection data are caricatures with exaggerated frequency deviation.

Image of FIG. 7.
FIG. 7.

Sample detections of acoustic scenes with differing degrees of clutter.

Image of FIG. 8.
FIG. 8.

(Color online) Cumulative density function for incorrect detections whose duration is less than or equal the duration indicated on the False Positive Duration axis. Both algorithms require that a hypothesized tonal have a duration ≥ 150 ms to be reported as a detection. The vast majority of false positive detections for both algorithms have short duration.

Image of FIG. 9.
FIG. 9.

Example of false positive detections caused by echosounders in both algorithms.


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Summary of recordings. Abbreviations: CalCOFI—California Cooperative Oceanic Fisheries Investigations oceanographic survey, SCI—San Clemente Island small boat survey, SOCAL—SOuthern CALifornia Instrumentation cruises on the R/V Sproul, FLIP—R/P FLIP moored recordings, and Palmyra—Palmyra Atoll small boat recordings.

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Audio files corresponding to the summary data of Table I. Files are publicly available in the Moby Sound archive as part of the 2011 Detection, Classification, and Localization of Marine Mammals Using Passive Acoustic Monitoring conference dataset.

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Performance comparison of graph and particle filter algorithms for the detection of odontocete whistle contours. Summary statistics are computed across all ground truth tonals meeting SNR and duration selection criteria (see text) and are not averages of sighting statistics. When given, ±σ indicates standard deviation.


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752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd
Scitation: Automated extraction of odontocete whistle contours