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Kate Stafford

Senior Principal Oceanographer

Affiliate Associate Professor, Oceanography

Email

stafford@apl.washington.edu

Phone

206-685-8617

Department Affiliation

Acoustics

Education

B.A. French Literature, Minor: Biology, University of California - Santa Cruz, 1989

M.S. Wildlife Biology, Oregon State University, 1995

Ph.D. Interdisciplinary Oceanography, Oregon State University, 2001

Publications

2000-present and while at APL-UW

Detecting, classifying, and counting but whale calls with Siamese neural networks

Zhong, M., M. Torterotot, T.A. Branch, K.M. Stafford, J.-Y. Royer, R. Dodhia, and J. Lavista, "Detecting, classifying, and counting but whale calls with Siamese neural networks," J. Acoust. Soc. Am., 149, doi:10.1121/10.0004828, 2021.

More Info

6 May 2021

The goal of this project is to use acoustic signatures to detect, classify, and count the calls of four acoustic populations of blue whales so that, ultimately, the conservation status of each population can be better assessed. We used manual annotations from 350 h of audio recordings from the underwater hydrophones in the Indian Ocean to build a deep learning model to detect, classify, and count the calls from four acoustic song types. The method we used was Siamese neural networks (SNN), a class of neural network architectures that are used to find the similarity of the inputs by comparing their feature vectors, finding that they outperformed the more widely used convolutional neural networks (CNN). Specifically, the SNN outperform a CNN with 2% accuracy improvement in population classification and 1.7%–6.4% accuracy improvement in call count estimation for each blue whale population. In addition, even though we treat the call count estimation problem as a classification task and encode the number of calls in each spectrogram as a categorical variable, SNN surprisingly learned the ordinal relationship among them. SNN are robust and are shown here to be an effective way to automatically mine large acoustic datasets for blue whale calls.

An open access dataset for developing automated detectors of Antarctic baleen whale sounds and performance evaluation of two commonly used detectors

Miller, B.S., and 15 others including K.M. Stafford, "An open access dataset for developing automated detectors of Antarctic baleen whale sounds and performance evaluation of two commonly used detectors," Sci. Rep., 11, doi:10.1038/s41598-020-78995-8, 2021.

More Info

12 Jan 2021

Since 2001, hundreds of thousands of hours of underwater acoustic recordings have been made throughout the Southern Ocean south of 60° S. Detailed analysis of the occurrence of marine mammal sounds in these circumpolar recordings could provide novel insights into their ecology, but manual inspection of the entirety of all recordings would be prohibitively time consuming and expensive. Automated signal processing methods have now developed to the point that they can be applied to these data in a cost-effective manner. However training and evaluating the efficacy of these automated signal processing methods still requires a representative annotated library of sounds to identify the true presence and absence of different sound types. This work presents such a library of annotated recordings for the purpose of training and evaluating automated detectors of Antarctic blue and fin whale calls. Creation of the library has focused on the annotation of a representative sample of recordings to ensure that automated algorithms can be developed and tested across a broad range of instruments, locations, environmental conditions, and years. To demonstrate the utility of the library, we characterise the performance of two automated detection algorithms that have been commonly used to detect stereotyped calls of blue and fin whales. The availability of this library will facilitate development of improved detectors for the acoustic presence of Southern Ocean blue and fin whales. It can also be expanded upon to facilitate standardization of subsequent analysis of spatiotemporal trends in call-density of these circumpolar species.

An accidental acoustician

Stafford, K., "An accidental acoustician," Whalewatcher, J. Am. Cetacean Soc., 43, 19-22, 2020.

1 Nov 2020

More Publications

In The News

Noise pollution is harming sea life, needs to be prioritized, scientists say

Reuters, Sharon Bernstein

Oceanographer Kate Stafford, commenting on a comprehensive review of over 500 research papers published in Science says, "The review makes it clear that, to actually reduce anthrophony (human noise) and aim for a well-managed future, ... we will need global cooperation among governments."

4 Feb 2021

The changing acoustic environment of the Arctic

Interalia Magazine, Richard Bright

In a wide-ranging discussion, Kate Stafford describes how she has worked in marine habitats all over the world, from the tropics to the poles, and is fortunate enough to have seen (and recorded) blue whales in every ocean in which they occur. She and Bright focus on current research on the changing acoustic environment of the Arctic and how changes from declining sea ice to increasing industrial human use may be influencing subarctic and Arctic marine mammals.

19 Jan 2021

What the whales hear

For the Wild Podcast, Host, Ayana Young

In this episode of For the Wild with Dr. Kate Stafford, we listen to the many songs the ocean body sings, asking; how does a warming climate alter the Arctic’s soundscape? Why are the waters of the Arctic becoming louder, and what does this mean for kin like the bowhead?

2 Sep 2020

More News Items

Acoustics Air-Sea Interaction & Remote Sensing Center for Environmental & Information Systems Center for Industrial & Medical Ultrasound Electronic & Photonic Systems Ocean Engineering Ocean Physics Polar Science Center
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