{"id":45,"date":"2017-04-29T04:14:43","date_gmt":"2017-04-29T04:14:43","guid":{"rendered":"http:\/\/roch.sdsu.edu\/?page_id=45"},"modified":"2025-12-30T09:40:17","modified_gmt":"2025-12-30T17:40:17","slug":"software","status":"publish","type":"page","link":"https:\/\/roch.sdsu.edu\/index.php\/software\/","title":{"rendered":"Software"},"content":{"rendered":"
<\/div>
<\/div><\/div>
<\/div><\/div><\/div>

Tethys<\/h2>\n

Tethys is a scientific workbench that allows the integration of acoustic detections, classifications, and localizations of aquatic animals with environmental data from a wide variety of souces. We and our collaborators built Tethys as we saw the opportunity to build systems that will let us analyze long-term data sets. Frequently, people analyze a few seasons worth of data and publish the results. While the summary results are archived in the publication, detailed results that could be used in subsequent analyses are lost. Tethys lets researchers preserve detailed information and provides simple acces to environmental data. Learn more at the Tethys web site<\/a>.<\/p>\n<\/div>

Annotating frequency modulated calls<\/i><\/h2>\n

Our group has worked in the area of automated annotation for toothed-whale whistles. We have created a MATLAB padckage silbido<\/em> that enables bioacousticians to use some of the algorithms that we have published. In particular, our initial graph search algorithm and our first deep-learning annotator are availble on GitHub in the <\/i>silbido <\/i>repository<\/a>. Silbido<\/i> contains components written in MATLAB, Java, and C++. When cloning the repository, you must compile the Java and C++ code, instructions on how to do this are in the documentation directory. Windows users can download one of the silbido<\/i> releases in which the code has already been compiled.<\/p>\n

The methods used are described in:<\/p>\n

P. Conant. P. Li, X. Liu, H. Klinck, E. Fleishman, D. Gillespie, E.-M. Nosal, and M. A. Roch (2022). \u201cSilbido profundo: An open source package for the use of deep learning to detect odontocete whistles,\u201d J. Acoustical Soc. Am., 152(6), pp. 3800-3808. doi:10.1121\/10.0016631.<\/span><\/p>\n

Li, P., Liu, X., Palmer, K. J., Fleishman, E., Gillespie, D., Nosal, E.-M., Shiu , Y., Klinck, H., Cholewiak, D., Helble, T., and Roch, M. A. (2020). \u201cLearning Deep Models from Synthetic Data for Extracting Dolphin Whistle Contours,\u201d in Intl. Joint Conf. Neural Net. (Glasgow, Scotland, July 19-24), pp. 10. DOI: 10.1109\/IJCNN48605.2020.9206992<\/p>\n

M. A. Roch, T.S. Brandes, B. Patel, Y. Barkley, S. Baumann-Pickering, M.S. Soldevilla, \u201cAutomated extraction of odontocete whistle contours,\u201d J. Acous. Soc. Am., Vol. 130(4), pp. 2212-2223, 2011. doi:10.1121\/1.3624821<\/a>.<\/span><\/p>\n

Requirements<\/strong>: Matlab 2021b or later (might work with 2019), Signal and Image processing toolboxes, free ONNX toolbox to use the deep learning annotator.<\/p>\n

Although not yet integrated into the MATLAB silbido package<\/i>, we have generated a transformer-based peak predictor that is used with silbido’s <\/i>tracking algorithm. It is described in:<\/p>\n

P. Li, X. Liu, H. Klinck, P. Gruden, M. A. Roch (2023), \u201cUsing deep learning to track time \u00d7 frequency whistle contours of toothed whales without human-annotated training data,\u201d J. Acoust. Soc. Am., 154(1), pp. 502-517. doi:10.1121\/10.0020274.<\/p>\n

and is available on github<\/a>. <\/p>\n

Public data sets<\/i><\/p>\n

The data used to deveop silbido <\/i>consists of towed array, dipping hydrophone, and stationary platform data recorded in the Southern California Bight and Palmyra Atoll. A subset of these data were annotated by trained analysts using a cubic spline annotation tool that is part of silbido<\/i>. These data were released as the DCLDE 2011 conference data set. They have recently been added to the National Centers for Environmental Information data archive:<\/p>\n

M. A. Roch, Y. Barkley, X. Zhang, M. S. Soldevilla, S. Baumann-Pickering, and J. A. Hildebrand, “DCLDE 2011 Conference Data,” NOAA National Centers for Environmental Information, 2025. doi:10.25921\/wsnc-js16<\/a>.<\/p>\n

The dataset also includes code in Python, Java, and MATLAB for reading and writing the tonal annotation files.<\/p>\n

A terrestrial data set has been developed from collar and boom microphone data for meerkats:<\/p>\n

J. Schaefer-Zimmermann, V. Demartsev, B. Averly, K. Dhanjal-Adams, M. Duteil, G. Gall, M. Fai\u00df, L. Johnson-Ulrich, D. Stowell, M. B. Manser, M. A. Roch, and A. Strandburg-Peshkin, \u201cMeerKAT: Meerkat Kalahari Audio Transcripts,\u201d Edmond Open Research Data Repository, The Max Planck Society, 2024, DOI: 10.17617\/3.0J0DYB.<\/p>\n<\/div>

<\/div>
<\/div><\/div><\/div>
<\/div><\/div><\/div><\/div><\/div>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-45","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/roch.sdsu.edu\/index.php\/wp-json\/wp\/v2\/pages\/45","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/roch.sdsu.edu\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/roch.sdsu.edu\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/roch.sdsu.edu\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/roch.sdsu.edu\/index.php\/wp-json\/wp\/v2\/comments?post=45"}],"version-history":[{"count":11,"href":"https:\/\/roch.sdsu.edu\/index.php\/wp-json\/wp\/v2\/pages\/45\/revisions"}],"predecessor-version":[{"id":633,"href":"https:\/\/roch.sdsu.edu\/index.php\/wp-json\/wp\/v2\/pages\/45\/revisions\/633"}],"wp:attachment":[{"href":"https:\/\/roch.sdsu.edu\/index.php\/wp-json\/wp\/v2\/media?parent=45"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}