Machine Learning Systems for Multimodal Affect Recognition
Markus Kächele offers a detailed view on the different steps in the affective computing pipeline, ranging from corpus design and recording over annotation and feature extraction to post-processing, classification of individual modalities and fusion in the
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Machine Learning Systems for Multimodal Affect Recognition
Machine Learning Systems for Multimodal Affect Recognition
Markus Kächele
Machine Learning Systems for Multimodal Affect Recognition
Markus Kächele Walzenhausen, Switzerland Dissertation at the Faculty of Engineering, Computer Sciences and Psychology, Ulm University, Germany, 2019
ISBN 978-3-658-28673-6 ISBN 978-3-658-28674-3 (eBook) https://doi.org/10.1007/978-3-658-28674-3 Springer Vieweg © Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2020 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer Vieweg imprint is published by the registered company Springer Fachmedien Wiesbaden GmbH part of Springer Nature. The registered company address is: Abraham-Lincoln-Str. 46, 65189 Wiesbaden, Germany
For Gerhard
Acknowledgments
Writing these final lines means that the work is almost finished. A work in which the effort of the last roughly five years culminates. The essence of my research formulated and written down. This work would have never been possible without the help, advice and support of a great deal of people. I want to start with my supervisors PD Dr. Friedhelm Schwenker and Prof. Dr. ¨ Gunther Palm for their invaluable support during the course of the PhD (but also already during my Diploma studies). Their knowledge, experience and keen eyes for mistakes in equations helped me a great deal and without them, this work would never exist. At this point, my deep gratitude goes towards the Transregional Collaborative Research Centre SFB/TRR 62 Companion-Technology for Cognitive Technical Sys¨ ¨ tems and the Landesgraduiertenforderung Baden-Wurttemberg which funded the research found in this thesis, allowed me to present my work on international conferences and supported me with a scholarship. Next, I would like to thank my colleagues for the pleasant time in the institute.
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