Christopher Hauer’s research while affiliated with Friedrich-Alexander-University Erlangen-Nürnberg and other places

What is this page?


This page lists works of an author who doesn't have a ResearchGate profile or hasn't added the works to their profile yet. It is automatically generated from public (personal) data to further our legitimate goal of comprehensive and accurate scientific recordkeeping. If you are this author and want this page removed, please let us know.

Publications (2)


Investigating the Effects of Selective Information Presentation in Intensive Care Units Using Virtual Reality
  • Conference Paper

October 2023

·

24 Reads

·

Fynn-Lennardt Metzler

·

·

[...]

·


A depiction of the towed hydrophone Streamers from previous fieldwork expeditions⁴⁶. For each hydrophone array, the (x, y, z)-coordinates with respect to the array origin are provided in Fig. 2. ±a\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\pm a$$\end{document} and e illustrate the azimuth and elevation angle of the array. The arrays were deployed 29.3 m behind the vessel during prior field trips, during the DLFW22 expedition the new DeepAL array was deployed only 28 m behind the vessel.
Depictions of the four hydrophone arrays utilized in this study. The x-, y- and z-coordinates display the distance of a hydrophone to the geometrical array center in meters. The geometrical center of an array also depicts the origin of the coordinate system.
Killer whale call types, interfering/added boat noise, and the chirp signal, all of them utilized in the experimental setup of ORCA-SPY.
Position of orca (SL = 156 dB re. 1 μ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mu$$\end{document}Pa p-p) and interfering boat noise (NL1 = 167 dB re. 1 μ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mu$$\end{document}Pa p-p or NL2 = 170 dB re. 1 μ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mu$$\end{document}Pa p-p, constant position of 41∘\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^\circ$$\end{document}). Depicted are the three followings examples. (1) An orca at 90∘\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$90^\circ$$\end{document}, 1000 m distance, and 0 m depth with a noise interference of NL2 would result in an SNR of −14.0, (2) An orca at 195∘\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$195^\circ$$\end{document}, 800 m distance, and 200 m depth with a noise interference of NL1 would result in an SNR of −9.3, and (3) An orca at 320∘\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$320^\circ$$\end{document}, 600 m distance, and 0 m depth with a noise interference of NL1 would result in an SNR of −6.6, see also Supplementary Figure S1 and Supplementary Table S6.
Raw Data depictions of the strong electronic interference during the lake Stechlin expedition on channel two on the left. The same time frame of channel one is depicted on the right for reference.

+5

ORCA-SPY enables killer whale sound source simulation, detection, classification and localization using an integrated deep learning-based segmentation
  • Article
  • Full-text available

July 2023

·

333 Reads

·

2 Citations

Acoustic identification of vocalizing individuals opens up new and deeper insights into animal communications, such as individual-/group-specific dialects, turn-taking events, and dialogs. However, establishing an association between an individual animal and its emitted signal is usually non-trivial, especially for animals underwater. Consequently, a collection of marine species-, array-, and position-specific ground truth localization data is extremely challenging, which strongly limits possibilities to evaluate localization methods beforehand or at all. This study presents ORCA-SPY, a fully-automated sound source simulation, classification and localization framework for passive killer whale (Orcinus orca) acoustic monitoring that is embedded into PAMGuard, a widely used bioacoustic software toolkit. ORCA-SPY enables array- and position-specific multichannel audio stream generation to simulate real-world ground truth killer whale localization data and provides a hybrid sound source identification approach integrating ANIMAL-SPOT, a state-of-the-art deep learning-based orca detection network, followed by downstream Time-Difference-Of-Arrival localization. ORCA-SPY was evaluated on simulated multichannel underwater audio streams including various killer whale vocalization events within a large-scale experimental setup benefiting from previous real-world fieldwork experience. Across all 58,320 embedded vocalizing killer whale events, subject to various hydrophone array geometries, call types, distances, and noise conditions responsible for a signal-to-noise ratio varying from -14.214.2-14.2 dB to 3 dB, a detection rate of 94.0 % was achieved with an average localization error of 7.01∘^\circ. ORCA-SPY was field-tested on Lake Stechlin in Brandenburg Germany under laboratory conditions with a focus on localization. During the field test, 3889 localization events were observed with an average error of 29.19∘^\circ and a median error of 17.54∘^\circ. ORCA-SPY was deployed successfully during the DeepAL fieldwork 2022 expedition (DLFW22) in Northern British Columbia, with a mean average error of 20.01∘^\circ and a median error of 11.01∘^\circ across 503 localization events. ORCA-SPY is an open-source and publicly available software framework, which can be adapted to various recording conditions as well as animal species.

Download

Citations (1)


... Binary-class segmentation using deep learning reduces the occurrence of false alarms and, as a result, decreases localization mistakes [36]. Advanced learning techniques have greatly enhanced the current best performance in automatically handling and comprehending various forms of data, such as images and text. ...

Reference:

Compliance Source Authentication Technique for Person Adaptation Networks Utilizing Deep Learning-Based Patterns Segmentation
ORCA-SPY enables killer whale sound source simulation, detection, classification and localization using an integrated deep learning-based segmentation