What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Speech recognition
- Machine learning
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.
His most cited work include:
- Opensmile: the munich versatile and fast open-source audio feature extractor (1251 citations)
- Recent developments in openSMILE, the munich open-source multimedia feature extractor (800 citations)
- The Geneva Minimalistic Acoustic Parameter Set (GeMAPS) for Voice Research and Affective Computing (528 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Speech recognition (63.33%)
- Artificial intelligence (42.00%)
- Recurrent neural network (24.00%)
What were the highlights of his more recent work (between 2015-2020)?
- Speech recognition (63.33%)
- Artificial intelligence (42.00%)
- Laughter (3.33%)
In recent papers he was focusing on the following fields of study:
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.
Between 2015 and 2020, his most popular works were:
- The Geneva Minimalistic Acoustic Parameter Set (GeMAPS) for Voice Research and Affective Computing (528 citations)
- Emotion-awareness for intelligent vehicle assistants: a research agenda (16 citations)
- The expression of emotion in the singing voice: Acoustic patterns in vocal performance (15 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Speech recognition
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.
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