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Christian Bergler, M. Eng.

  • Job title: Researcher
  • Organization: Department of Computer Science
  • Working group: Chair of Computer Science 5 (Pattern Recognition)
  • Phone number: +49 9131 85 27872
  • Fax number: +49 9131 85 27270
  • Email: christian.bergler@fau.de
  • Website:
  • Address:
    Martensstr. 3
    91058 Erlangen
    Room 10.134

For marine biologists, the interpretation and understanding of underwater audio recordings is essential. Based on such recordings, possible conclusions about behavior, communication and social interactions of marine animals can be made. Despite a large number of biological studies on the subject of orca vocalizations, it is still difficult to recognize a structure or semantic significance of the orca signals in order to be able to derive any patterns of behavior.

Orca

Northern Resident Killer Whale during the fieldwork expedition 2018/2019 in Vancouver Island, British Columbia, Canada (copyright by Anthro-Media)

 

Due to a lack of  techniques and computational tools, hundreds of hours of underwater recordings are still listened to by marine biologists in order to detect potential orca vocalizations. In a post process these identified orca signals will be analyzed and categorized. The main goal is to provide a robust method which is able to automatically detect orca calls within underwater audio recordings.

Orca

An Orca fin and its grey Saddle Patch form the “Face of an Orca” during the fieldwork expedition 2018/2019 in Vancouver Island, British Columbia, Canada (copyright by Anthro-Media)

 

A robust detection of orca signals in connection with the associated situational video recordings and behaviour descriptions (provided by several researchers on site) can provide potential information about communication (kind of a language model) and behaviors (e.g. hunting, socializing). Furthermore, the orca signal detection algorithm can be used in conjunction with a localization software to provide the researchers on the field a more efficient way of searching orca populations. For more information about the project please contact me at christian.bergler@fau.de.

Orca Pod

Orca family (Matriline/Pod) of several motherly related Individuals during the fieldwork expedition 2018/2019 in Vancouver Island, British Columbia, Canada (copyright by Anthro-Media)

 

Since 04/2018: PhD Student, Pattern Recognition Lab, Friedrich-Alexander University Erlangen-Nürnberg

  • Research focus on Deep Learning Applied to Animal Linguistics in particular the analysis of underwater audio recordings of marine animals (killer whales)

04/2016 – 03/2018: Software Engineer and external PhD Student (FAU), e.sigma Technology GmbH, Ilmenau

  • Software developer for communication, voice processing, and guidance systems (automated speech recognition, text-to-speech systems, language model adaption)

03/2014 – 03/2016: Master of Engineering in Information Technology and Automation, Ostbayerische Technical University of Applied Science (OTH), Amberg – Weiden, Location: Amberg

  • Focus on information technology
  • Master thesis: Parametrization of various speaker characteristics for the generation of emotions within a High Quality Limited Domain Text – to – Speech – System (HQLD-TTS)

10/2010 – 03/2014: Bachelor of Engineering in Industrial Engineering, Ostbayerische Technical University of Applied Science (OTH), Amberg – Weiden, Location: Weiden

  • Focus on mechanical engineering
  • Bachelor thesis: Development of a freeTTS-based speech-synthesis system for the training of air traffic controllers

09/2009 – 07/2010: Vocational Diploma (Fachabitur), Berufsoberschule (BOS), Schwandorf

 

 

Teaching Experience

  • since 03/2019: PhD Student, Friedrich-Alexander University Erlangen-Nürnberg (FAU)
    • Exercises: Introduction to Pattern Recognition (IntroPR), Deep Learning
    • Seminar: Automatische Analyse von Stimm-, Sprech- und Sprachstörungen bei Sprachpathologien
    • Praktikum: Representation Learning
  • 10/2011 – 06/2012: Student teaching assistant, Ostbayerische Technical University of Applied Science (OTH), Amberg – Weiden, Location: Weiden
    • Modules: Mathematics/Statistics, Data Processing and Programming, Physics, Industrial Economics

 

 

Practical Experience

  • 04/2016 – 03/2018: Software Engineer, e.sigma Technology GmbH, Ilmenau
    • Automatic Speech Recognition (ASR) for Air Traffic Control (ATC)
    • Controller Pilot Data Link Communication (CPDLC)
    • Advanced Voice Processing (Text – to – Speech (TTS) Engine, limited Domain TTS Voice Creation)
    • Communication Framework (VoIP Standard)
    • Realtime Audio and Video Streaming
    • Distributed System Communication via Message Broker Systems
  • 08/2013 – 11/2015: Student trainee, e.sigma Technology GmbH, Ilmenau
    • Software development, testing, maintenance, integration and documentation for the java-based freeTTS text-to-speech engine
  • 08/2012 – 01/2013: Student trainee, Continental Automotive GmbH, Regensburg
    • Support programming, integration and verification of Manufacturing Execution Systems (=MES) for all worldwide Continental
      Chassis & Safety plants

2018

  • Deep Learning Applied to Animal Linguistics


    (FAU Funds)
    Term: April 1, 2018 - April 1, 2022
    Deep Learning Applied to Animal Linguistics in particular the analysis of underwater audio recordings of marine animals (killer whales):

    For marine biologists, the interpretation and understanding of underwater audio recordings is essential. Based on such recordings, possible conclusions about behaviour, communication and social interactions of marine animals can be made. Despite a large number of biological studies on the subject of orca vocalizations, it is still difficult to recognize a structure or semantic/syntactic significance of orca signals in order to be able to derive any language and/or behavioral patterns. Due to a lack of techniques and computational tools, hundreds of hours of underwater recordings are still manually verified by marine biologists in order to detect potential orca vocalizations. In a post process these identified orca signals are analyzed and categorized. One of the main goals is to provide a robust and automatic method which is able to automatically detect orca calls within underwater audio recordings. A robust detection of orca signals is the baseline for any further and deeper analysis. Call type identification and classification based on pre-segmented signals can be used in order to derive semantic and syntactic patterns. In connection with the associated situational video recordings and behaviour descriptions (provided by several researchers on site) can provide potential information about communication (kind of a language model) and behaviors (e.g. hunting, socializing). Furthermore, orca signal detection can be used in conjunction with a localization software in order to provide researchers on the field with a more efficient way of searching the animals as well as individual recognition.

    For more information about the DeepAL project please contact christian.bergler@fau.de.

2019

Journal Articles

Conference Contributions

Seminar (SEM)

Kolloquium (KO)

Vorlesung (VORL)

Übung (UE)