Search Results for author: Lea Schumann

Found 5 papers, 3 papers with code

A Physiologically-Adapted Gold Standard for Arousal during Stress

no code implementations27 Jul 2021 Alice Baird, Lukas Stappen, Lukas Christ, Lea Schumann, Eva-Maria Meßner, Björn W. Schuller

We utilise a Long Short-Term Memory, Recurrent Neural Network to explore the benefit of fusing these physiological signals with arousal as the target, learning from various audio, video, and textual based features.

The MuSe 2021 Multimodal Sentiment Analysis Challenge: Sentiment, Emotion, Physiological-Emotion, and Stress

1 code implementation14 Apr 2021 Lukas Stappen, Alice Baird, Lukas Christ, Lea Schumann, Benjamin Sertolli, Eva-Maria Messner, Erik Cambria, Guoying Zhao, Björn W. Schuller

Multimodal Sentiment Analysis (MuSe) 2021 is a challenge focusing on the tasks of sentiment and emotion, as well as physiological-emotion and emotion-based stress recognition through more comprehensively integrating the audio-visual, language, and biological signal modalities.

Emotion Recognition Multimodal Sentiment Analysis

MuSe 2020 -- The First International Multimodal Sentiment Analysis in Real-life Media Challenge and Workshop

1 code implementation30 Apr 2020 Lukas Stappen, Alice Baird, Georgios Rizos, Panagiotis Tzirakis, Xinchen Du, Felix Hafner, Lea Schumann, Adria Mallol-Ragolta, Björn W. Schuller, Iulia Lefter, Erik Cambria, Ioannis Kompatsiaris

Multimodal Sentiment Analysis in Real-life Media (MuSe) 2020 is a Challenge-based Workshop focusing on the tasks of sentiment recognition, as well as emotion-target engagement and trustworthiness detection by means of more comprehensively integrating the audio-visual and language modalities.

Emotion Recognition Multimodal Sentiment Analysis

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