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Björn Schuller
Researcher at Imperial College London
Publications - 1086
Citations - 44692
Björn Schuller is an academic researcher from Imperial College London. The author has contributed to research in topics: Computer science & Recurrent neural network. The author has an hindex of 84, co-authored 929 publications receiving 34713 citations. Previous affiliations of Björn Schuller include Technische Universität München & University of Tokyo.
Papers
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Proceedings ArticleDOI
Opensmile: the munich versatile and fast open-source audio feature extractor
TL;DR: The openSMILE feature extraction toolkit is introduced, which unites feature extraction algorithms from the speech processing and the Music Information Retrieval communities and has a modular, component based architecture which makes extensions via plug-ins easy.
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Recent developments in openSMILE, the munich open-source multimedia feature extractor
TL;DR: OpenSMILE 2.0 as mentioned in this paper unifies feature extraction paradigms from speech, music, and general sound events with basic video features for multi-modal processing, allowing for time synchronization of parameters, on-line incremental processing as well as off-line and batch processing, and the extraction of statistical functionals (feature summaries).
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The Geneva Minimalistic Acoustic Parameter Set (GeMAPS) for Voice Research and Affective Computing
Florian Eyben,Klaus R. Scherer,Björn Schuller,Johan Sundberg,Elisabeth André,Carlos Busso,Laurence Devillers,Julien Epps,Petri Laukka,Shrikanth S. Narayanan,Khiet P. Truong +10 more
TL;DR: A basic standard acoustic parameter set for various areas of automatic voice analysis, such as paralinguistic or clinical speech analysis, is proposed and intended to provide a common baseline for evaluation of future research and eliminate differences caused by varying parameter sets or even different implementations of the same parameters.
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New Avenues in Opinion Mining and Sentiment Analysis
TL;DR: The history, current use, and future of opinion mining and sentiment analysis are discussed, along with relevant techniques and tools.
Proceedings Article
The INTERSPEECH 2009 Emotion Challenge
TL;DR: The challenge, the corpus, the features, and benchmark results of two popular approaches towards emotion recognition from speech, and the FAU Aibo Emotion Corpus are introduced.