`Whoop' - new autonomous method precisely detects endangered whale vocalizations

Date:
September 15, 2021
Source:
Florida Atlantic University
Summary:
One of the frequently used methods to monitor endangered whales
is called passive acoustics technology, which doesn't always
perform well. In the increasingly noisy ocean, current methods can
mistake other sounds for whale calls. This high 'false positive'
rate hampers scientific research and hinders conservation
efforts. Researchers used artificial intelligence and machine
learning methods to develop a new and much more accurate method
of detecting Right whale up-calls -- a short 'whoop' sound that
lasts about two seconds.



FULL STORY ==========================================================================
The North Atlantic Right Whale (Right whale) is one of the most endangered whale species in the world with only about 368 remaining off the east
coast of North America. A decreasing trend and low reproduction rates,
combined with high levels of human activities -- such as shipping and
fisheries -- underscore their precarious situation. Efficient tracking of
their numbers, migration paths and habitat use is vital to lowering the
number of preventable injuries and deaths and promoting their recovery.


==========================================================================
One of the frequently used methods to monitor whales is called passive acoustics technology. Right whales vocalize a variety of low-frequency
sounds such as moans, groans, pulses and even belches. One typical
vocalization they use to communicate with each other is referred to as
an "up-call," which is a short chirp or "whoop" that lasts about two
seconds. Up-calls are narrowband vocalizations with frequency swings
in the range of 50 to 440 Hertz and appear to function as signals that
bring whales together.

Although current passive acoustics technology is a reliable, safe and
effective way to monitor these endangered leviathans, it hasn't always performed well. In the increasingly noisy ocean, current methods can
mistake other sounds for whale calls. This high "false positive" rate
hampers scientific research and hinders conservation efforts.

Researchers from Florida Atlantic University's Harbor Branch Oceanographic Institute and the College of Engineering and Computer Science used
artificial intelligence (AI) and machine learning methods to develop a
new and much more accurate method of detecting Right whale up-calls. The technology utilizes Multimodal Deep Learning (MMDL) algorithms to evaluate acoustic recordings and make decisions on the presence of up-calls.

The study's findings, published in the Journal of the Acoustical Society
of America, showed that the MMDL detector outperformed conventional
machine learning methods and demonstrated the superiority of the MMDL
algorithm in terms of the up-call detection rate, non-up-call detection
rate, and false alarm rate. The autonomy of the MMDL detector has
immediate application for effectively monitoring and protecting Right
whales where accurate call detection of a low-density species is critical.

"Our deep learning algorithm is a significant advancement on conventional machine learning methods. The near zero false-positive, false-negative
and false alarm rates indicate that this new MMDL detector could be
a powerful tool in the detection and monitoring of the low density,
endangered North Atlantic Right Whale, especially in environments
with high acoustic-masking," said Laurent M. Che'rubin, Ph.D., senior
author and a research professor at FAU Harbor Branch who worked with
Ali K. Ibrahim, first author and a post-doctoral research associate at
FAU Harbor Branch. "Since the attributes of the MMDL system are not
signal specific, we believe that it can be used as a classifier for
all applications in which multiple classes are involved." Researchers
verified the effectiveness of the MMDL model for Right whale up- call
detection with Cornell University's dataset. These recorded signals were converted to images and classified by the MMDL detector. The algorithm, composed of two types of neural networks, randomly selects its design parameters, requires little preprocessing and automates its architecture construction. Outputs from individual models are evaluated by a fusion classifier, which selects the most probable outcome.

To highlight the urgency for effective detection and monitoring
technologies in endangered species, new research is indicating that
whales and other marine species are being impacted by climate change,
including shifts in migration patterns and habitat use. Recently, North Atlantic Right Whales have been observed in locations not previously
known as important Right whale habitat.

The new MMDL system offers a new tool to effectively monitor and assess
the importance of these new behaviors in a changing ocean.

Study co-authors are Hanqi Zhuang, Ph.D., chair of the Department of
Electrical Engineering and Computer Science, FAU College of Engineering
and Computer Science; Nurgun Erdol, Ph.D., professor, Department of
Electrical Engineering and Computer Science, FAU College of Engineering
and Computer Science; and Gregory O'Corry-Crowe, Ph.D., lead of the
Wildlife Evolution and Behavior program (WEB) and a research professor
at FAU Harbor Branch.

This research was supported in part by the National Science Foundation
(MRI Grant No. 1828181), which provided the scientists with the necessary computing equipment. Staff support was provided by the Protect Florida
Whales Specialty License Plate provided through the Harbor Branch
Oceanographic Institute Foundation.

========================================================================== Story Source: Materials provided by Florida_Atlantic_University. Original written by Gisele Galoustian. Note: Content may be edited for style
and length.


========================================================================== Journal Reference:
1. Ali K Ibrahim, Hanqi Zhuang, Laurent M. Che'rubin, Nurgun Erdol,
Gregory
O'Corry-Crowe, Ali Muhamed Ali. A multimodel deep learning
algorithm to detect North Atlantic right whale up-calls. The
Journal of the Acoustical Society of America, 2021; 150 (2):
1264 DOI: 10.1121/10.0005898 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2021/09/210915095430.htm

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