Model-based localization of deep-diving cetaceans using towed line array acoustic data

Passive acoustic monitoring using a towed line array of hydrophones is a standard method for localizing cetaceans during line-transect cetacean abundance surveys. Perpendicular distances estimated between localized whales and the trackline are essential for abundance estimation using acoustic data. Uncertainties in the acoustic data from hydrophone movement, sound propagation effects, errors in the time of arrival differences, and whale depth are not accounted for by most two-dimensional localization methods. Consequently, location and distance estimates for deep-diving cetaceans may be biased, creating uncertainty in abundance estimates. Here, a model-based localization approach is applied to towed line array acoustic data that incorporates sound propagation effects, accounts for sources of error, and localizes in three dimensions. The whale’s true distance, ship trajectory, and whale movement greatly affected localization results in simulations. The localization method was applied to real acoustic data from two separate sperm whales, resulting in three-dimensional distance and depth estimates with position bounds for each whale. By incorporating sources of error, this three-dimensional model-based approach provides a method to address and integrate the inherent uncertainties in towed array acoustic data for more robust localization

Silbido profundo : an open source package for the use of deep learning to detect odontocete whistles

The authors wish to thank Dr. Michael Weise of the Office of Naval Research (N00014-17-1-2867, N00014-17-1-2567) for supporting this project. We also thank Anu Kumar and Mandy Shoemaker of U.S. Navy Living Marine Resources for supporting development of the data management tools used in this work (N3943020C2202).This work presents an open-source matlab software package for exploiting recent advances in extracting tonal signals from large acoustic data sets. A whistle extraction algorithm published by Li, Liu, Palmer, Fleishman, Gillespie, Nosal, Shiu, Klinck, Cholewiak, Helble, and Roch [(2020). Proceedings of the International Joint Conference on Neural Networks, July 19–24, Glasgow, Scotland, p. 10] is incorporated into silbido, an established software package for extraction of cetacean tonal calls. The precision and recall of the new system were over 96% and nearly 80%, respectively, when applied to a whistle extraction task on a challenging two-species subset of a conference-benchmark data set. A second data set was examined to assess whether the algorithm generalized to data that were collected across different recording devices and locations. These data included 487 h of weakly labeled, towed array data collected in the Pacific Ocean on two National Oceanographic and Atmospheric Administration (NOAA) cruises. Labels for these data consisted of regions of toothed whale presence for at least 15 species that were based on visual and acoustic observations and not limited to whistles. Although the lack of per whistle-level annotations prevented measurement of precision and recall, there was strong concurrence of automatic detections and the NOAA annotations, suggesting that the algorithm generalizes well to new data.Publisher PDFPeer reviewe

Deep neural networks for automated detection of marine mammal species

Authors thank the Bureau of Ocean Energy Management for the funding of MARU deployments, Excelerate Energy Inc. for the funding of Autobuoy deployment, and Michael J. Weise of the US Office of Naval Research for support (N000141712867).Deep neural networks have advanced the field of detection and classification and allowed for effective identification of signals in challenging data sets. Numerous time-critical conservation needs may benefit from these methods. We developed and empirically studied a variety of deep neural networks to detect the vocalizations of endangered North Atlantic right whales (Eubalaena glacialis). We compared the performance of these deep architectures to that of traditional detection algorithms for the primary vocalization produced by this species, the upcall. We show that deep-learning architectures are capable of producing false-positive rates that are orders of magnitude lower than alternative algorithms while substantially increasing the ability to detect calls. We demonstrate that a deep neural network trained with recordings from a single geographic region recorded over a span of days is capable of generalizing well to data from multiple years and across the species’ range, and that the low false positives make the output of the algorithm amenable to quality control for verification. The deep neural networks we developed are relatively easy to implement with existing software, and may provide new insights applicable to the conservation of endangered species.Publisher PDFPeer reviewe

Improve automatic detection of animal call sequences with temporal context

Funding: This work was supported by the US Office of Naval Research (grant no. N00014-17-1-2867).Many animals rely on long-form communication, in the form of songs, for vital functions such as mate attraction and territorial defence. We explored the prospect of improving automatic recognition performance by using the temporal context inherent in song. The ability to accurately detect sequences of calls has implications for conservation and biological studies. We show that the performance of a convolutional neural network (CNN), designed to detect song notes (calls) in short-duration audio segments, can be improved by combining it with a recurrent network designed to process sequences of learned representations from the CNN on a longer time scale. The combined system of independently trained CNN and long short-term memory (LSTM) network models exploits the temporal patterns between song notes. We demonstrate the technique using recordings of fin whale (Balaenoptera physalus) songs, which comprise patterned sequences of characteristic notes. We evaluated several variants of the CNN + LSTM network. Relative to the baseline CNN model, the CNN + LSTM models reduced performance variance, offering a 9-17% increase in area under the precision-recall curve and a 9-18% increase in peak F1-scores. These results show that the inclusion of temporal information may offer a valuable pathway for improving the automatic recognition and transcription of wildlife recordings.Publisher PDFPeer reviewe

Learning deep models from synthetic data for extracting dolphin whistle contours

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.Postprin

Wind, waves, and acoustic background levels at Station ALOHA

Author Posting. © American Geophysical Union, 2012. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research 117 (2012): C03017, doi:10.1029/2011JC007267.Frequency spectra from deep-ocean near-bottom acoustic measurements obtained contemporaneously with wind, wave, and seismic data are described and used to determine the correlations among these data and to discuss possible causal relationships. Microseism energy appears to originate in four distinct regions relative to the hydrophone: wind waves above the sensors contribute microseism energy observed on the ocean floor; a fraction of this local wave energy propagates as seismic waves laterally, and provides a spatially integrated contribution to microseisms observed both in the ocean and on land; waves in storms generate microseism energy in deep water that travels as seismic waves to the sensor; and waves reflected from shorelines provide opposing waves that add to the microseism energy. Correlations of local wind speed with acoustic and seismic spectral time series suggest that the local Longuet-Higgins mechanism is visible in the acoustic spectrum from about 0.4 Hz to 80 Hz. Wind speed and acoustic levels at the hydrophone are poorly correlated below 0.4 Hz, implying that the microseism energy below 0.4 Hz is not typically generated by local winds. Correlation of ocean floor acoustic energy with seismic spectra from Oahu and with wave spectra near Oahu imply that wave reflections from Hawaiian coasts, wave interactions in the deep ocean near Hawaii, and storms far from Hawaii contribute energy to the seismic and acoustic spectra below 0.4 Hz. Wavefield directionality strongly influences the acoustic spectrum at frequencies below about 2 Hz, above which the acoustic levels imply near-isotropic surface wave directionality.Funding for the ALOHA Cabled Observatory was provided by the National Science Foundation and the State of Hawaii through the School of Ocean and Earth Sciences and Technology at the University of Hawaii-Manoa (F. Duennebier, PI). Donations from AT&T and TYCOM and the cooperation of the U.S. Navy made this project possible. The WHOI-Hawaii Ocean Time series Station (WHOTS) mooring is maintained by Woods Hole Oceanographic Institution (PIs R. Weller and A. Plueddemann) with funding from the NOAA Climate Program Office/Climate Observation Division. NSF grant OCE- 0926766 supported R. Lukas (co-PI) to augment and collaborate on the maintenance of WHOTS. Lukas was also supported during this analysis by The National Ocean Partnership Program “Advanced Coupled Atmosphere-Wave-Ocean Modeling for Improving Tropical Cyclone Prediction Models” under contract N00014-10-1-0154 to the University of Rhode Island (I. Ginis, PI).2012-09-1

Room sound field prediction by acoustical radiosity

Acoustical radiosity is a technique based on assumptions of diffuse reflection and incoherent phase relationships that has been used to predict room sound fields. In this research, the background to acoustical radiosity is given, the integral equation (on which the technique is based) is derived, and a numerical solution is detailed for convex rooms of arbitrary shape. Several validations are made by comparison of the numerical solution to (1) analytical solutions for a sphere; (2) results from a ray tracing algorithm in cubical enclosures, and; (3) measurements in three real rooms.Science, Faculty ofMathematics, Department ofGraduat