An official website of the United States government
The vocalization behavior of humpback whales in the Norwegian and Barents Seas is examined based on recordings of a large-aperture, densely-populated coherent hydrophone array system. The passive ocean acoustic waveguide remote sensing (POAWRS) technique is employed to provide detection, bearing-time estimation, time-frequency characterization and classification of the humpback whale vocalizations. The song vocalizations, composed of highly structured and repeatable set of phrases, were detected throughout the diel cycle between February 18 to March 8, 2014. The beamformed spectrograms of the detected humpback vocalizations are classified as song sequences based on inter-pulse intervals and time-frequency characteristics, verified by visual inspection. The song structure is compared for humpback whale vocalizations recorded at three distinct regions off the Norwegian coast, Alesund, Lofoten and Northern Finmark. Multiple bearing-time trajectories for humpback songs were simultaneously observed indicating multiple singers present at each measurement site. Humpback whale received call rates and temporo-spatial distributions are compared across the three measurement sites. Geographic mapping of humpback whale calls from their bearing-time trajectories is accomplished via the moving array triangulation technique.
more »
« less
Classifying Humpback Whale Calls to Song and Non-Song Vocalizations using Bag of Words Descriptor on Acoustic Data
Humpback whale behavior, population distribution and structure can be inferred from long term underwater passive acoustic monitoring of their vocalizations. Here we develop automatic approaches for classifying humpback whale vocalizations into the two categories of song and non-song, employing machine learning techniques. The vocalization behavior of humpback whales was monitored over instantaneous vast areas of the Gulf of Maine using a large aperture coherent hydrophone array system via the passive ocean acoustic waveguide remote sensing technique over multiple diel cycles in Fall 2006. We use wavelet signal denoising and coherent array processing to enhance the signal-to-noise ratio. To build features vector for every time sequence of the beamformed signals, we employ Bag of Words approach to time-frequency features. Finally, we apply Support Vector Machine (SVM), Neural Networks, and Naive Bayes to classify the acoustic data and compare their performances. Best results are obtained using Mel Frequency Cepstrum Coefficient (MFCC) features and SVM which leads to 94% accuracy and 72.73% F1-score for humpback whale song versus non-song vocalization classification, showing effectiveness of the proposed approach for real-time classification at sea.
more »
« less
- Award ID(s):
- 1736749
- PAR ID:
- 10198587
- Date Published:
- Journal Name:
- 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)
- Page Range / eLocation ID:
- 865 to 870
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
A large variety of sound sources in the ocean, including biological, geophysical, and man-made, can be simultaneously monitored over instantaneous continental-shelf scale regions via the passive ocean acoustic waveguide remote sensing (POAWRS) technique by employing a large-aperture densely-populated coherent hydrophone array system. Millions of acoustic signals received on the POAWRS system per day can make it challenging to identify individual sound sources. An automated classification system is necessary to enable sound sources to be recognized. Here, the objectives are to (i) gather a large training and test data set of fin whale vocalization and other acoustic signal detections; (ii) build multiple fin whale vocalization classifiers, including a logistic regression, support vector machine (SVM), decision tree, convolutional neural network (CNN), and long short-term memory (LSTM) network; (iii) evaluate and compare performance of these classifiers using multiple metrics including accuracy, precision, recall and F1-score; and (iv) integrate one of the classifiers into the existing POAWRS array and signal processing software. The findings presented here will (1) provide an automatic classifier for near real-time fin whale vocalization detection and recognition, useful in marine mammal monitoring applications; and (2) lay the foundation for building an automatic classifier applied for near real-time detection and recognition of a wide variety of biological, geophysical, and man-made sound sources typically detected by the POAWRS system in the ocean.more » « less
-
Halliday, William David (Ed.)Among tremendous biodiversity within the California Current Ecosystem (CCE) are gigantic mysticetes (baleen whales) that produce structured sequences of sound described as song. From six years of passive acoustic monitoring within the central CCE we measured seasonal and interannual variations in the occurrence of blue (Balaenoptera musculus), fin (Balaenoptera physalus), and humpback (Megaptera novaeangliae) whale song. Song detection during 11 months of the year defines its prevalence in this foraging habitat and its potential use in behavioral ecology research. Large interannual changes in song occurrence within and between species motivates examination of causality. Humpback whales uniquely exhibited continuous interannual increases, rising from 34% to 76% of days over six years, and we examine multiple hypotheses to explain this exceptional trend. Potential influences of physical factors on detectability – including masking and acoustic propagation – were not supported by analysis of wind data or modeling of acoustic transmission loss. Potential influences of changes in local population abundance, site fidelity, or migration timing were supported for two of the interannual increases in song detection, based on extensive local photo ID data (17,356 IDs of 2,407 individuals). Potential influences of changes in foraging ecology and efficiency were supported across all years by analyses of the abundance and composition of forage species. Following detrimental food web impacts of a major marine heatwave that peaked during the first year of the study, foraging conditions consistently improved for humpback whales in the context of their exceptional prey-switching capacity. Stable isotope data from humpback and blue whale biopsy samples are consistent with observed interannual variations in the regional abundance and composition of forage species. This study thus indicates that major interannual changes in detection of baleen whale song may reflect underlying variations in forage species availability driven by energetic variations in ecosystem state.more » « less
-
An eight-element oil-filled hydrophone array is used to measure the acoustic field in littoral waters. This prototype array was deployed during an experiment between Jeffrey’s Ledge and the Stellwagen Bank region off the coast of Rockport, Massachusetts USA. During the experiment, several humpback whale vocalizations, distant ship tonals and high frequency conventional echosounder pings were recorded. Visual confirmation of humpback moving in bearing relative to the array verifies the directional sensing from array beamforming. During deployment, the array is towed at speeds varying from 4-7 kts in water depths of roughly 100 m with conditions at sea state 2 to 3. This array system consists of a portable winch with array, tow cable and 3 water-resistant boxes housing electronics. This system is deployed and operated by 2 crew members onboard a 13 m commercial fishing vessel during the experiment. Non-acoustic sensor (NAS) information is obtained to provide depth, temperature, and heading data using commercial off the shelf (COTS) components utilizing RS485/232 data communications. Acoustic data sampling was performed at 8 kHz, 30 kHz and 100 kHz with near real-time processing of data and enhanced Signal to Noise Ratio (SNR) from beamforming. The electrical system components are deployed with 3 stacked electronics boxes housing power, data acquisition and data processing components in water resistant compartments. A laptop computer with 8 TB of external storage and an independent Global Positioning System (GPS) antenna is used to run Passive Ocean Acoustic Waveguide Remote Sensing (POAWRS) software providing beamformed spectrogram data and live NAS data with capability of capturing several days of data. The acquisition system consists of Surface Mount Device (SMD) pre-amplifiers with filter to an analog differential pair shipboard COTS acquisition system. Pre-amplifiers are constructed using SMD technology where components are pressure tolerant and potting is not necessary. Potting of connectors, electronics and hydrophones via 3D printed molding techniques will be discussed. Array internal components are manufactured with Thermoplastic Polyurethane (TPU) 3D printed material to dampen array vibrations with forward and aft vibration isolation modules (VIM). Polyurethane foam (PUF) used to scatter breathing waves and dampen contact from wires inside the array without attenuating high frequencies and allowing for significant noise reduction. A single Tygon array section with a length of 7.5 m and diameter of 38 mm contains 8 transducer elements with a spacing of 75 cm (1 kHz design frequency). Pre- amplifiers and NAS modules are affixed using Vectran and steel wire rope positioned by swaged stops along the strength member. The tow cable length is 100 m with a diameter of 22 mm that is potted to a hose adapter to break out 12 braided copper wire twisted pair conductors and terminates the tow cable Vectran braid. This array in its current state of development is a low-cost alternative to obtain quality acoustic data from a towed array system. Used here for observation of whale vocalizations, this type of array also has many applications in military sonar and seismic surveying. Maintenance on the array can be performed without the use of special facilities or equipment for dehosing and conveniently uses castor oil as an environmentally safe pressure compensating and coupling fluid. Array development including selection of transducers, NAS modules, acoustic acquisition system, array materials and method of construction with results from several deployments will be discussed. We also present beamformed spectrograms containing humpback whale downsweep moans and underwater blowing (bubbles) sounds associated with feeding on sand lance (Ammodytes dubius).more » « less
-
Quantifying how animals respond to disturbance events bears relevance for understanding consequences to population health. We investigate whether blue whales respond acoustically to naturally occurring episodic noise by examining calling before and after earthquakes (27 040 calls, 32 earthquakes; 27 January–29 June 2016). Two vocalization types were evaluated: New Zealand blue whale song and downswept vocalizations ('D calls'). Blue whales did not alter the number of D calls, D call received level or song intensity following earthquakes (paired t -tests, p > 0.7 for all). Linear models accounting for earthquake strength and proximity revealed significant relationships between change in calling activity surrounding earthquakes and prior calling activity (D calls: R 2 = 0.277, p < 0.0001; song: R 2 = 0.080, p = 0.028); however, these same relationships were true for ‘null’ periods without earthquakes (D calls: R 2 = 0.262, p < 0.0001; song: R 2 = 0.149, p = 0.0002), indicating that the pattern is driven by blue whale calling context regardless of earthquake presence. Our findings that blue whales do not respond to episodic natural noise provide context for interpreting documented acoustic responses to anthropogenic noise sources, including shipping traffic and petroleum development, indicating that they potentially evolved tolerance for natural noise sources but not novel noise from anthropogenic origins.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICMLA.2019.00150
