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.
With this in mind, the MuSe-Toolbox provides the functionality to run exhaustive searches for meaningful class clusters in the continuous gold standards.
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.
Truly real-life data presents a strong, but exciting challenge for sentiment and emotion research.
1 code implementation • 30 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.