Generalized marine mammal detection based on improved band-limited processing

Bougher, Benjamin B.; Hood, Joey; Theriault, James A.; Moors, Hilary B.

doi:10.1121/1.4773596

Cited by 5 publications

(4 citation statements)

References 8 publications

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“…The bandlimited detection processing method described in Ref. 30 was used to detect vocalizations in the longer acoustic record. This method calculated a detection function by estimating a short-term energy average in the desired signal band and dividing by a longer average of noise energy.…”

Section: F Data Preparation and Detection Processmentioning

confidence: 99%

Automated aural classification used for inter-species discrimination of cetaceans

Binder

Hines

2014

The Journal of the Acoustical Society of America

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Passive acoustic methods are in widespread use to detect and classify cetacean species; however, passive acoustic systems often suffer from large false detection rates resulting from numerous transient sources. To reduce the acoustic analyst workload, automatic recognition methods may be implemented in a two-stage process. First, a general automatic detector is implemented that produces many detections to ensure cetacean presence is noted. Then an automatic classifier is used to significantly reduce the number of false detections and classify the cetacean species. This process requires development of a robust classifier capable of performing inter-species classification. Because human analysts can aurally discriminate species, an automated aural classifier that uses perceptual signal features was tested on a cetacean data set. The classifier successfully discriminated between four species of cetaceans-bowhead, humpback, North Atlantic right, and sperm whales-with 85% accuracy. It also performed well (100% accuracy) for discriminating sperm whale clicks from right whale gunshots. An accuracy of 92% and area under the receiver operating characteristic curve of 0.97 were obtained for the relatively challenging bowhead and humpback recognition case. These results demonstrated that the perceptual features employed by the aural classifier provided powerful discrimination cues for inter-species classification of cetaceans.

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Section: F Data Preparation and Detection Processmentioning

confidence: 99%

Automated aural classification used for inter-species discrimination of cetaceans

Binder

Hines

2014

The Journal of the Acoustical Society of America

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“…Since many PAM systems rely on the time-frequency characteristics of the vocalizations for detection and classification [1,4,19,20,21,22,23,24], signal distortion has the potential to negatively affect the accuracy of PAM systems [10,22,25]; however, little research has been directed towards this problem. Some authors acknowledge that propagation effects likely impact the accuracy of PAM systems, but do not analyze how their system is affected.…”

Section: List Of Abbreviations and Symbols Usedmentioning

confidence: 99%

“…Spectrograms of example bowhead and humpback calls are shown in Figure 3.1. The similarity of calls between these two species makes it difficult for some automatic detectors (e.g., the band-limited energy detector discussed in Bougher et al [21] and Hood et al [53]) to discriminate between the species, Figure 3.1: Example spectrograms of (a) bowhead, and (b) synthetic bowhead vocalizations; as well as the humpback song units referred to as (c) humpback1, (d) humpback2, (e) humpback3, (f) humpback4, and (g) an example synthetic humpback vocalization. Spectrograms are produced using Hann-weighted windows of length 512 and 256 samples for the bowhead and humpback vocalizations, respectively, and 70 % overlap.…”

Section: Recordings Of Example Bowhead and Humpback Vocalizations Wermentioning

confidence: 99%

Impacts of environment-dependent acoustic propagation on passive acoustic monitoring of cetaceans

Binder

Hines

2016

Journal of the Acoustical Society of America

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Significant effort has been made over the last few decades to develop automated passive acoustic monitoring (PAM) systems capable of classifying cetaceans at the species level; however, these systems often require tuning when deployed in different environments. Anecdotal evidence suggests that this requirement to adjust a PAM system's parameters is partially due to differences in the acoustic propagation characteristics. The environmentdependent propagation characteristics create variation in how a cetacean vocalization is distorted after it is emitted. If these difference are not accounted for it could reduce the performance of automated PAM systems. An aural classifier developed at Defence R&D Canada (DRDC) has been used successfully for inter-species discrimination of cetaceans. Accurate results are obtained by using perceptual signal features that model the features employed by the human auditory system. In this thesis, a combination of an at-sea experiment and simulations with modified bowhead and humpback whale vocalizations was conducted to investigate the robustness of the classifier performance to signal distortion as a function of propagation range. It was found that in many environments classification performance degraded with increasing range, largely due to decreased signal-to-noise ratio (SNR); however, in some environments as much as 40 % of the performance reduction was attributed to signal distortion resulting from environment-dependent propagation. It was found that sound speed profiles resulting in considerable boundary interaction were important for producing sufficient signal distortion to affect PAM performance, relative to the impacts of SNR. Therefore, in some environments the ocean acoustic properties should be taken into account when characterizing performance of automated PAM systems. For the environments in which signal-to-noise issues dominate, the use of multi-element arrays is expected to increase the performance of automated recognition systems beyond the minor improvements to be gained from adjusting a PAM system's parameters. Nonetheless, propagation modelling should be used to complement PAM experiments to account for bias in probability of detection estimates resulting from environment-dependent acoustic propagation.

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“…The whole process is divided into two steps. Firstly, automatic detection (Bougher et al, 2012) is applied to a dataset of four species of cetacean bowhead (Mellinger, Clark, 2000), humpback (Payne, McVay, 1971), North Atlantic right (Bort et al, 2015) and sperm whales (Thode et al, 2002). Secondly, automatic classifier is used to accurately distinguish between species which shows 85% accuracy but aural classifier was applied on a limited dataset (Young, Hines, 2007).…”

Section: Introductionmentioning

confidence: 99%

Archives of Acoustics

Nadir

Adnan

Aziz

et al. 2020

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Marine mammal identification and classification for passive acoustic monitoring remain a challenging task. Mainly the interspecific and intraspecific variations in calls within species and among different individuals of single species make it more challenging. Varieties of species along with geographical diversity induce more complications towards an accurate analysis of marine mammal classification using acoustic signatures. Prior methods for classification focused on spectral features which result in increasing bias for contour base classifiers in automatic detection algorithms. In this study, acoustic marine mammal classification is performed through the fusion of 1D Local Binary Pattern (1D-LBP) and Mel Frequency Cepstral Coefficient (MFCC) based features. Multi-class Support Vector Machines (SVM) classifier is employed to identify different classes of mammal sounds. Classification of six species named Tursiops truncatus, Delphinus delphis, Peponocephala electra, Grampus griseus, Stenella longirostris, and Stenella attenuate are targeted in this research. The proposed model achieved 90.4% accuracy on 70-30% training testing and 89.6% on 5-fold cross-validation experiments.

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Generalized marine mammal detection based on improved band-limited processing

Cited by 5 publications

References 8 publications

Automated aural classification used for inter-species discrimination of cetaceans

Automated aural classification used for inter-species discrimination of cetaceans

Impacts of environment-dependent acoustic propagation on passive acoustic monitoring of cetaceans

Archives of Acoustics

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