Active contour-based detection of estuarine dolphin whistles in spectrogram images

Serra, O. M.; Martins, Flavius Portella Ribas; Padovese, Linilson Rodrigues

doi:10.1016/j.ecoinf.2019.101036

Cited by 18 publications

(10 citation statements)

References 21 publications

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“…This would involve an accurate identification of each whistle. However, this task is complex, and detection techniques developed so far have not been able to solve this problem without errors [69][70][71][72][73][74][75][76][77][78][79][80][81][82]84,85]. If whistles are first identified, then a classification of whistles using unsupervised classification techniques such as UMAP [67] could be applied, also using a metric to determine the optimal number of clusters [95].…”

Section: Discussionmentioning

confidence: 99%

“…Thus, to detect and identify whistles, a vocalisation tracking method is necessary. Selecting vocalisations in an audio track is a complex task, and several techniques have already been developed to achieve it: statistical modelling of whistles [69][70][71][72][73][74], tracking algorithms based on hand-picked parameters [75][76][77], image processing approaches [78][79][80][81][82][83] or deep learning models associated with clustering methods [84,85]. For our dataset, we chose to adapt a tracking algorithm developed during the DECAV project [86].…”

Section: Vocalisation Detectionmentioning

confidence: 99%

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Behavioural Responses of Common Dolphins Delphinus delphis to a Bio-Inspired Acoustic Device for Limiting Fishery By-Catch

et al. 2022

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By-catch is the most direct threat to marine mammals globally. Acoustic repellent devices (pingers) have been developed to reduce dolphin by-catch. However, mixed results regarding their efficiency have been reported. Here, we present a new bio-inspired acoustic beacon, emitting returning echoes from the echolocation clicks of a common dolphin ‘Delphinus delphis’ from a fishing net, to inform dolphins of its presence. Using surface visual observations and the automatic detection of echolocation clicks, buzzes, burst-pulses and whistles, we assessed wild dolphins’ behavioural responses during sequential experiments (i.e., before, during and after the beacon’s emission), with or without setting a net. When the device was activated, the mean number of echolocation clicks and whistling time of dolphins significantly increased by a factor of 2.46 and 3.38, respectively (p < 0.01). Visual surface observations showed attentive behaviours of dolphins, which kept a distance of several metres away from the emission source before calmly leaving. No differences were observed among sequences for buzzes/burst-pulses. Our results highlight that this prototype led common dolphins to echolocate more and communicate differently, and it would favour net detection. Complementary tests of the device during the fishing activities of professional fishermen should further contribute to assessment of its efficiency.

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Section: Discussionmentioning

confidence: 99%

Section: Vocalisation Detectionmentioning

confidence: 99%

Behavioural Responses of Common Dolphins Delphinus delphis to a Bio-Inspired Acoustic Device for Limiting Fishery By-Catch

et al. 2022

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“…Trajectory-search methods seek peaks in the spectral energy of short consecutive segments and stitch neighboring peaks together on the basis of a trajectory estimate ( [1,8,10]). Other work has examined local context, performing ridge regressions [11] or building on ridge regression maps by using energy minimization algorithms to find contours followed by a final classification to reduce excessive numbers of false positives [12].…”

Section: A Whistle Contour Extractionmentioning

confidence: 99%

“…The second class, trajectory-search methods, seeks energy peaks along the frequency dimension and connects those peaks along the time dimension on the basis of trajectory estimation [12] [11] [37]. Improved trajectory-search methods reduce excessive numbers of false positives by applying ridge regression to local contexts [38] or energy minimization algorithms to ridge regression maps [39].…”

Section: Related Work a Whistle Contour Extractionmentioning

confidence: 99%

Learning Deep Models from Synthetic Data for Extracting Dolphin Whistle Contours

Liu

Palmer

et al. 2020

2020 International Joint Conference on Neural Networks (IJCNN)

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We present a learning-based method for extracting whistles of toothed whales (Odontoceti) in hydrophone recordings. Our method represents audio signals as time-frequency spectrograms and decomposes each spectrogram into a set of time-frequency patches. A deep neural network learns archetypical patterns (e.g., crossings, frequency modulated sweeps) from the spectrogram patches and predicts time-frequency peaks that are associated with whistles. We also developed a comprehensive method to synthesize training samples from background environments and train the network with minimal human annotation effort. We applied the proposed learn-from-synthesis method to a subset of the public Detection, Classification, Localization, and Density Estimation (DCLDE) 2011 workshop data to extract whistle confidence maps, which we then processed with an existing contour extractor to produce whistle annotations. The F1-score of our best synthesis method was 0.158 greater than our baseline whistle extraction algorithm (~25% improvement) when applied to common dolphin (Delphinus spp.) and bottlenose dolphin (Tursiops truncatus) whistles.

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“…For example, in the reference approach pursued by Gillespie et al ( 2013 ) three noise removal algorithms are first applied to the spectrogram of sound data, and then a connected region search is conducted to link together sections of the spectrogram which are above a pre-determined threshold and close in time and frequency. A similar technique exploits a probabilistic Hough transform algorithm to detect ridges similar to thick line segments, which are then adjusted to the geometry of the potential whistles in the image via an active contour algorithm (Serra et al, 2020 ). Other algorithmic methods aim at quantifying the variation in complexity (randomness) occurring in the acoustic time series containing the vocalization, for example by measuring signal entropy (Siddagangaiah et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Automated detection of dolphin whistles with convolutional networks and transfer learning

Korkmaz

Diamant

Gil

et al. 2023

Front. Artif. Intell.

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Effective conservation of maritime environments and wildlife management of endangered species require the implementation of efficient, accurate and scalable solutions for environmental monitoring. Ecoacoustics offers the advantages of non-invasive, long-duration sampling of environmental sounds and has the potential to become the reference tool for biodiversity surveying. However, the analysis and interpretation of acoustic data is a time-consuming process that often requires a great amount of human supervision. This issue might be tackled by exploiting modern techniques for automatic audio signal analysis, which have recently achieved impressive performance thanks to the advances in deep learning research. In this paper we show that convolutional neural networks can indeed significantly outperform traditional automatic methods in a challenging detection task: identification of dolphin whistles from underwater audio recordings. The proposed system can detect signals even in the presence of ambient noise, at the same time consistently reducing the likelihood of producing false positives and false negatives. Our results further support the adoption of artificial intelligence technology to improve the automatic monitoring of marine ecosystems.

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Active contour-based detection of estuarine dolphin whistles in spectrogram images

Cited by 18 publications

References 21 publications

Behavioural Responses of Common Dolphins Delphinus delphis to a Bio-Inspired Acoustic Device for Limiting Fishery By-Catch

Behavioural Responses of Common Dolphins Delphinus delphis to a Bio-Inspired Acoustic Device for Limiting Fishery By-Catch

Learning Deep Models from Synthetic Data for Extracting Dolphin Whistle Contours

Automated detection of dolphin whistles with convolutional networks and transfer learning

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