A generalized baleen whale call detection and classification system

Author Posting. © Acoustical Society of America, 2011. This article is posted here by permission of Acoustical Society of America for personal use, not for redistribution. The definitive version was published in Journal of the Acoustical Society of America 129 (2011): 2889-2902, doi:10.1121/1.3562166.Passive acoustic monitoring allows the assessment of marine mammal occurrence and distribution at greater temporal and spatial scales than is now possible with traditional visual surveys. However, the large volume of acoustic data and the lengthy and laborious task of manually analyzing these data have hindered broad application of this technique. To overcome these limitations, a generalized automated detection and classification system (DCS) was developed to efficiently and accurately identify low-frequency baleen whale calls. The DCS (1) accounts for persistent narrowband and transient broadband noise, (2) characterizes temporal variation of dominant call frequencies via pitch-tracking, and (3) classifies calls based on attributes of the resulting pitch tracks using quadratic discriminant function analysis (QDFA). Automated detections of sei whale (Balaenoptera borealis) downsweep calls and North Atlantic right whale (Eubalaena glacialis) upcalls were evaluated using recordings collected in the southwestern Gulf of Maine during the spring seasons of 2006 and 2007. The accuracy of the DCS was similar to that of a human analyst: variability in differences between the DCS and an analyst was similar to that between independent analysts, and temporal variability in call rates was similar among the DCS and several analysts.Funding for the fieldwork was provided by the NOAA NEFSC, WHOI Ocean Life Institute, and the WHOI John E. and Anne W. Sawyer Endowed Fund. Development of the detection and classification system was supported by a grant from the Office of Naval Research

ORCA-SPOT: An Automatic Killer Whale Sound Detection Toolkit Using Deep Learning

Large bioacoustic archives of wild animals are an important source to identify reappearing communication patterns, which can then be related to recurring behavioral patterns to advance the current understanding of intra-specific communication of non-human animals. A main challenge remains that most large-scale bioacoustic archives contain only a small percentage of animal vocalizations and a large amount of environmental noise, which makes it extremely difficult to manually retrieve sufficient vocalizations for further analysis – particularly important for species with advanced social systems and complex vocalizations. In this study deep neural networks were trained on 11,509 killer whale (Orcinus orca) signals and 34,848 noise segments. The resulting toolkit ORCA-SPOT was tested on a large-scale bioacoustic repository – the Orchive – comprising roughly 19,000 hours of killer whale underwater recordings. An automated segmentation of the entire Orchive recordings (about 2.2 years) took approximately 8 days. It achieved a time-based precision or positive-predictive-value (PPV) of 93.2% and an area-under-the-curve (AUC) of 0.9523. This approach enables an automated annotation procedure of large bioacoustics databases to extract killer whale sounds, which are essential for subsequent identification of significant communication patterns. The code will be publicly available in October 2019 to support the application of deep learning to bioaoucstic research. ORCA-SPOT can be adapted to other animal species

Cetacean presence on the northern Mid Atlantic Ridge revealed through passive acoustic monitoring

Cetaceans are known to utilise the Mid-Atlantic Ridge, a key topographical feature in the Atlantic Ocean, for migratory and feeding purposes. Passive acoustic monitoring was used over a one-year period (2007/2008) to identify cetacean vocalisations that occurred on a location near the Charlie Gibbs Fracture Zone. Using species-specific vocalisations that have previously been documented in the North Atlantic, six cetacean species were identified: fin whales, blue whales, sei whales, humpback whales, minke whales and sperm whales; and three non- biological soundscape components were also identified: earthquakes, airguns from seismic exploration and shipping vessels. Call types from fin whales (20 Hz pulse and 40 Hz downsweep) and blue whales (A-B call and D-call) were identified separately, to explore intraspecific call variation. Temporal trends were identified in blue whales, sperm whales and humpback whales; whereas minke whales did not display a clear presence pattern. Fin whales and sei whales were present year-round. Fin whale 20 Hz pulses showed a peak in detections during winter, as did the 40 Hz downsweep, despite the low audible area ranges during these times due to strong fin whale choruses. The sei whale downsweep, however, was relatively constant throughout the entire year. Environmental and biological variables did not appear to explain much of the variation in cetacean presence, indicating that cetaceans use the MAR for migration purposes

Low frequency vocalizations attributed to sei whales (Balaenoptera borealis)

Author Posting. © Acoustical Society of America, 2008. This article is posted here by permission of Acoustical Society of America for personal use, not for redistribution. The definitive version was published in Journal of the Acoustical Society of America 124 (2008): 1339-1349, doi:10.1121/1.2945155.Low frequency (<100 Hz) downsweep vocalizations were repeatedly recorded from ocean gliders east of Cape Cod, MA in May 2005. To identify the species responsible for this call, arrays of acoustic recorders were deployed in this same area during 2006 and 2007. 70 h of collocated visual observations at the center of each array were used to compare the localized occurrence of this call to the occurrence of three baleen whale species: right, humpback, and sei whales. The low frequency call was significantly associated only with the occurrence of sei whales. On average, the call swept from 82 to 34 Hz over 1.4 s and was most often produced as a single call, although pairs and (more rarely) triplets were occasionally detected. Individual calls comprising the pairs were localized to within tens of meters of one another and were more similar to one another than to contemporaneous calls by other whales, suggesting that paired calls may be produced by the same animal. A synthetic kernel was developed to facilitate automatic detection of this call using spectrogram-correlation methods. The optimal kernel missed 14% of calls, and of all the calls that were automatically detected, 15% were false positives.Funding was provided by the NOAA National Marine Fisheries Service and the WHOI Ocean Life Institute

Detection of Whale Acoustic Signals in the Northern Gulf of Mexico LADC-GEMM Database

Low-pass Fourier filter, wavelet filter, as well as matched filter detection methods were used to detect baleen whale signals in northern Gulf of Mexico data collected by the Littoral Acoustic Demonstration Center (LADC) consortium. Some potential low frequency signals appeared on the matched filter output figure. The shape of the signals is in line with one of the typical signal shapes of fin whales--vertical down-sweeps with 18s-time interval. Another shape of the signals is in line with one of the call type shapes of Bryde\u27s whales--down-sweeps with 7s-time interval. A high-pass Fourier filter was also used to find toothed whale high frequency sounds in the Gulf of Mexico data. The sounds featuring click trains and codas belonging to sperm whales have been clearly identified

Detection of Whale Acoustic Signals in the Northern Gulf of Mexico LADC-GEMM Database

Low-pass Fourier filter, wavelet filter, as well as matched filter detection methods were used to detect baleen whale signals in northern Gulf of Mexico data collected by the Littoral Acoustic Demonstration Center (LADC) consortium. Some potential low frequency signals appeared on the matched filter output figure. The shape of the signals is in line with one of the typical signal shapes of fin whales--vertical down-sweeps with 18s-time interval. Another shape of the signals is in line with one of the call type shapes of Bryde\u27s whales--down-sweeps with 7s-time interval. A high-pass Fourier filter was also used to find toothed whale high frequency sounds in the Gulf of Mexico data. The sounds featuring click trains and codas belonging to sperm whales have been clearly identified

Real-time reporting of baleen whale passive acoustic detections from ocean gliders

Author Posting. © Acoustical Society of America, 2013. This article is posted here by permission of Acoustical Society of America for personal use, not for redistribution. The definitive version was published in Journal of the Acoustical Society of America 134 (2013): 1814-1823, doi:10.1121/1.4816406.In the past decade, much progress has been made in real-time passive acoustic monitoring of marine mammal occurrence and distribution from autonomous platforms (e.g., gliders, floats, buoys), but current systems focus primarily on a single call type produced by a single species, often from a single location. A hardware and software system was developed to detect, classify, and report 14 call types produced by 4 species of baleen whales in real time from ocean gliders. During a 3-week deployment in the central Gulf of Maine in late November and early December 2012, two gliders reported over 25 000 acoustic detections attributed to fin, humpback, sei, and right whales. The overall false detection rate for individual calls was 14%, and for right, humpback, and fin whales, false predictions of occurrence during 15-min reporting periods were 5% or less. Transmitted pitch tracks—compact representations of sounds—allowed unambiguous identification of both humpback and fin whale song. Of the ten cases when whales were sighted during aerial or shipboard surveys and a glider was within 20 km of the sighting location, nine were accompanied by real-time acoustic detections of the same species by the glider within ±12 h of the sighting time.The Office of Naval Research funded this work, with additional support provided by the NOAA Fisheries Advanced Sampling Technologies Working Group via the Cooperative Institute for the North Atlantic Region

Right whales up-call detection using deep classifiers over underwater noisy recordings

This project evaluates the potential of Convolutional Neural Networks in classifying Right Whales' Up-Calls from short audio clips of environmental sounds. Two deep models with different architectures are presented evaluating them over different preprocesses on the same dataset and different metrics

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

Persistent near real-time passive acoustic monitoring for baleen whales from a moored buoy: System description and evaluation

© The Author(s), 2019. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Baumgartner, M. F., Bonnell, J., Van Parijs, S. M., Corkeron, P. J., Hotchkin, C., Ball, K., Pelletier, L., Partan, J., Peters, D., Kemp, J., Pietro, J., Newhall, K., Stokes, A., Cole, T. V. N., Quintana, E., & Kraus, S. D. Persistent near real-time passive acoustic monitoring for baleen whales from a moored buoy: System description and evaluation. Methods in Ecology and Evolution, 10(9), (2019): 1476-1489, doi: 10.1111/2041-210X.13244.1. Managing interactions between human activities and marine mammals often relies on an understanding of the real‐time distribution or occurrence of animals. Visual surveys typically cannot provide persistent monitoring because of expense and weather limitations, and while passive acoustic recorders can monitor continuously, the data they collect are often not accessible until the recorder is recovered. 2. We have developed a moored passive acoustic monitoring system that provides near real‐time occurrence estimates for humpback, sei, fin and North Atlantic right whales from a single site for a year, and makes those occurrence estimates available via a publicly accessible website, email and text messages, a smartphone/tablet app and the U.S. Coast Guard's maritime domain awareness software. We evaluated this system using a buoy deployed off the coast of Massachusetts during 2015–2016 and redeployed again during 2016–2017. Near real‐time estimates of whale occurrence were compared to simultaneously collected archived audio as well as whale sightings collected near the buoy by aerial surveys. 3. False detection rates for right, humpback and sei whales were 0% and nearly 0% for fin whales, whereas missed detection rates at daily time scales were modest (12%–42%). Missed detections were significantly associated with low calling rates for all species. We observed strong associations between right whale visual sightings and near real‐time acoustic detections over a monitoring range 30–40 km and temporal scales of 24–48 hr, suggesting that silent animals were not especially problematic for estimating occurrence of right whales in the study area. There was no association between acoustic detections and visual sightings of humpback whales. 4. The moored buoy has been used to reduce the risk of ship strikes for right whales in a U.S. Coast Guard gunnery range, and can be applied to other mitigation applications.We thank Annamaria Izzi, Danielle Cholewiak and Genevieve Davis of the NOAA NEFSC for assistance in developing the analyst protocol. We are grateful to the NOAA NEFSC aerial survey observers (Leah Crowe, Pete Duley, Jen Gatzke, Allison Henry, Christin Khan and Karen Vale) and the NEAq aerial survey observers (Angela Bostwick, Marianna Hagbloom and Paul Nagelkirk). Danielle Cholewiak and three anonymous reviewers provided constructive criticism on earlier drafts of the manuscript. Funding for this project was provided by the NOAA NEFSC, NOAA Advanced Sampling Technology Work Group, Environmental Security Technology Certification Program of the U.S. Department of Defense, the U.S. Navy's Living Marine Resources Program, Massachusetts Clean Energy Center and the Bureau of Ocean Energy Management. Funding from NOAA was facilitated by the Cooperative Institute for the North Atlantic Region (CINAR) under Cooperative Agreement NA14OAR4320158