Florian Eyben mainly investigates Speech recognition, Affective computing, Artificial intelligence, Valence and Arousal. His Speech recognition study combines topics from a wide range of disciplines, such as Artificial neural network, Recurrent neural network, Autoencoder and Feature extraction. His Feature extraction research includes elements of Speech processing, Voice activity detection and Support vector machine.
His studies in Artificial intelligence integrate themes in fields like Machine learning and Natural language processing. His Valence research incorporates elements of Emotion recognition and Audio visual. The concepts of his Mel-frequency cepstrum study are interwoven with issues in Formant, Loudness and Signal processing.
Florian Eyben mainly focuses on Speech recognition, Artificial intelligence, Recurrent neural network, Pattern recognition and Natural language processing. His study in Speech recognition is interdisciplinary in nature, drawing from both Artificial neural network, Valence, Feature extraction and Feature. His Feature extraction study integrates concerns from other disciplines, such as Audio signal processing and Speech processing.
His work on Support vector machine and Affective computing as part of general Artificial intelligence study is frequently connected to Dynamic Bayesian network, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them. The Recurrent neural network study combines topics in areas such as Time delay neural network, Voice activity detection and Robustness. The various areas that Florian Eyben examines in his Pattern recognition study include Filter, Feature and Audio signal.
His primary areas of study are Speech recognition, Artificial intelligence, Laughter, Recurrent neural network and Natural language processing. Many of his research projects under Speech recognition are closely connected to Musical expression with Musical expression, tying the diverse disciplines of science together. His work in Artificial intelligence addresses issues such as Pattern recognition, which are connected to fields such as Emotion recognition.
His studies deal with areas such as Robot, Syntax and Feature vector as well as Laughter. His work in Recurrent neural network covers topics such as Speech corpus which are related to areas like Kernel, Filter bank, Deep learning and Artificial neural network. Florian Eyben interconnects Cluster analysis, Personality and Voice activity detection in the investigation of issues within Natural language processing.
His main research concerns Speech recognition, Cognitive psychology, Set, Real time tracking and Multimedia. His study on Spectrogram is often connected to Uncompressed video as part of broader study in Speech recognition. His Cognitive psychology research integrates issues from Sadness, Singing, Feature vector and Laughter.
His work deals with themes such as Dynamics, Loudness, Emotional expression and Human voice, which intersect with Set.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Opensmile: the munich versatile and fast open-source audio feature extractor
Florian Eyben;Martin Wöllmer;Björn Schuller.
acm multimedia (2010)
Recent developments in openSMILE, the munich open-source multimedia feature extractor
Florian Eyben;Felix Weninger;Florian Gross;Björn Schuller.
acm multimedia (2013)
The INTERSPEECH 2013 computational paralinguistics challenge: social signals, conflict, emotion, autism
Björn W. Schuller;Stefan Steidl;Anton Batliner;Alessandro Vinciarelli.
conference of the international speech communication association (2013)
The Geneva Minimalistic Acoustic Parameter Set (GeMAPS) for Voice Research and Affective Computing
Florian Eyben;Klaus R. Scherer;Bjorn W. Schuller;Johan Sundberg.
IEEE Transactions on Affective Computing (2016)
OpenEAR — Introducing the munich open-source emotion and affect recognition toolkit
Florian Eyben;Martin Wollmer;Bjorn Schuller.
affective computing and intelligent interaction (2009)
Abandoning Emotion Classes - Towards Continuous Emotion Recognition with Modelling of Long-Range Dependencies
Martin Wöllmer;Florian Eyben;Stephan Reiter;Björn W. Schuller.
conference of the international speech communication association (2008)
AVEC 2011-the first international audio/visual emotion challenge
Björn Schuller;Michel Valstar;Florian Eyben;Gary McKeown.
affective computing and intelligent interaction (2011)
AVEC 2013: the continuous audio/visual emotion and depression recognition challenge
Michel Valstar;Björn Schuller;Kirsty Smith;Florian Eyben.
acm multimedia (2013)
Cross-Corpus Acoustic Emotion Recognition: Variances and Strategies
B Schuller;B Vlasenko;F Eyben;Martin Wöllmer.
IEEE Transactions on Affective Computing (2010)
AVEC 2012: the continuous audio/visual emotion challenge
Björn Schuller;Michel Valster;Florian Eyben;Roddy Cowie.
international conference on multimodal interfaces (2012)
Profile was last updated on December 6th, 2021.
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