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Scopus Author ID: 56407163900

Keywords

natural language processing, opinion mining, applied machine learning, affective computing

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Germany

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Employment (1)

Technische Universität München: Munich, Bavaria, DE

2018-08-01 to present | Research Fellow (Department of Informatics)
Employment
Source: Self-asserted source
Gerhard Johann Hagerer

Education and qualifications (1)

Technische Universität München: Munich, Bavaria, DE

2015-10-15 to 2016-05-15 | Master of Science in Computer Science (Department of Informatics)
Qualification
Source: Self-asserted source
Gerhard Johann Hagerer

Works (16)

Predicting Individual Well-Being in Teamwork Contexts Based on Speech Features

Information
2024-04-12 | Journal article
DOI: 10.3390/info15040217
Contributors: Tobias Zeulner; Gerhard Johann Hagerer; Moritz Müller; Ignacio Vazquez; Peter A. Gloor
Source: check_circle
Crossref
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Preferred source (of 2)‎

Predicting Team Well-Being through Face Video Analysis with AI

Applied Sciences
2024-02-03 | Journal article
DOI: 10.3390/app14031284
Contributors: Moritz Müller; Ambre Dupuis; Tobias Zeulner; Ignacio Vazquez; Johann Hagerer; Peter A. Gloor
Source: check_circle
Crossref
grade
Preferred source (of 2)‎

Opinion Mining for Qualitative Content Studies

2023 | Dissertation or Thesis
Contributors: Hagerer, Gerhard Johann
Source: Self-asserted source
Gerhard Johann Hagerer

The news media and its audience: Agenda setting on organic food in the United States and Germany

Journal of Cleaner Production
2022-06 | Journal article
DOI: 10.1016/j.jclepro.2022.131503
Contributors: Hannah Danner; Gerhard Hagerer; Yan Pan; Georg Groh
Source: check_circle
Crossref

Unsupervised graph-based topic modeling from video transcriptions

arXiv
2021 | Preprint
EID:

2-s2.0-85107798293

Part of ISSN: 23318422
Source: Self-asserted source
Gerhard Johann Hagerer
grade
Preferred source (of 2)‎

An evaluation of progressive neural networks for transfer learning in natural language processing

LREC 2020 - 12th International Conference on Language Resources and Evaluation, Conference Proceedings
2020 | Conference paper
EID:

2-s2.0-85096617848
Contributors: Hagerer, G.; Moeed, A.; Dugar, S.; Gupta, S.; Ghosh, M.; Danner, H.; Mitevski, O.; Nawroth, A.; Groh, G.
Source: Self-asserted source
Gerhard Johann Hagerer via Scopus - Elsevier

Evaluation metrics for headline generation using deep pre-trained embeddings

LREC 2020 - 12th International Conference on Language Resources and Evaluation, Conference Proceedings
2020 | Conference paper
EID:

2-s2.0-85096565629
Contributors: Moeed, A.; An, Y.; Hagerer, G.; Groh, G.
Source: Self-asserted source
Gerhard Johann Hagerer via Scopus - Elsevier

Robust laughter detection for wearable wellbeing sensing

ACM International Conference Proceeding Series
2018 | Conference paper
DOI: 10.1145/3194658.3194693
EID:

2-s2.0-85047192910
Contributors: Hagerer, G.; Cummins, N.; Eyben, F.; Schuller, B.
Source: Self-asserted source
Gerhard Johann Hagerer via Scopus - Elsevier

VoicePlay - An affective sports game operated by speech emotion recognition based on the component process model

2017 7th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos, ACIIW 2017
2018 | Conference paper
DOI: 10.1109/ACIIW.2017.8272589
EID:

2-s2.0-85047237692
Contributors: Hagerer, G.; Eyben, F.; Schuller, D.; Scherer, K.R.; Schuller, B.
Source: Self-asserted source
Gerhard Johann Hagerer via Scopus - Elsevier

"did you laugh enough today?" -deep neural networks for mobile andwearable laughter trackers

Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
2017 | Conference paper
DOI: 10.21437/Interspeech.2017-2036
EID:

2-s2.0-85039171477

Part of ISSN: 19909772 2308457X
Contributors: Hagerer, G.; Cummins, N.; Eyben, F.; Schuller, B.
Source: Self-asserted source
Gerhard Johann Hagerer via Scopus - Elsevier

An image-based deep spectrum feature representation for the recognition of emotional speech

MM 2017 - Proceedings of the 2017 ACM Multimedia Conference
2017 | Conference paper
DOI: 10.1145/3123266.3123371
EID:

2-s2.0-85035242905
Contributors: Cummins, N.; Amiriparian, S.; Hagerer, G.; Batliner, A.; Steidl, S.; Schuller, B.W.
Source: Self-asserted source
Gerhard Johann Hagerer via Scopus - Elsevier

Automatic multi-lingual arousal detection from voice applied to real product testing applications

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
2017 | Conference paper
DOI: 10.1109/ICASSP.2017.7953139
EID:

2-s2.0-85023771959

Part of ISSN: 15206149
Contributors: Eyben, F.; Unfried, M.; Hagerer, G.; Schuller, B.
Source: Self-asserted source
Gerhard Johann Hagerer via Scopus - Elsevier

Enhancing LSTM rnn-based speech overlap detection by artificially mixed data

Proceedings of the AES International Conference
2017 | Conference paper
EID:

2-s2.0-85028085834
Contributors: Hagerer, G.; Pandit, V.; Eyben, F.; Schuller, B.
Source: Self-asserted source
Gerhard Johann Hagerer via Scopus - Elsevier

Real-time tracking of speakers' emotions, states, and traits on mobile platforms

Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
2016 | Conference paper
EID:

2-s2.0-84994316599

Part of ISSN: 19909772 2308457X
Contributors: Marchi, E.; Eyben, F.; Hagerer, G.; Schuller, B.
Source: Self-asserted source
Gerhard Johann Hagerer via Scopus - Elsevier

Augmenting affect from speech with generative music

Conference on Human Factors in Computing Systems - Proceedings
2015 | Conference paper
DOI: 10.1145/2702613.2732792
EID:

2-s2.0-84954228067
Contributors: Hagerer, G.J.; Lux, M.; Ehrlich, S.; Cheng, G.
Source: Self-asserted source
Gerhard Johann Hagerer via Scopus - Elsevier

Fuguegenerator - Collaborative melody composition based on a generative approach for conveying emotion in music

Proceedings - 40th International Computer Music Conference, ICMC 2014 and 11th Sound and Music Computing Conference, SMC 2014 - Music Technology Meets Philosophy: From Digital Echos to Virtual Ethos
2014 | Conference paper
EID:

2-s2.0-84908888825
Contributors: Klügel, N.; Hagerer, G.; Groh, G.
Source: Self-asserted source
Gerhard Johann Hagerer via Scopus - Elsevier
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