S
Stefan Goetze
Researcher at Fraunhofer Society
Publications - 83
Citations - 1208
Stefan Goetze is an academic researcher from Fraunhofer Society. The author has contributed to research in topics: Speech enhancement & Reverberation. The author has an hindex of 18, co-authored 73 publications receiving 1046 citations. Previous affiliations of Stefan Goetze include University of Oldenburg & University of Bremen.
Papers
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Proceedings ArticleDOI
On the use of spectro-temporal features for the IEEE AASP challenge ‘detection and classification of acoustic scenes and events’
Jens Schroder,Niko Moritz,Marc René Schädler,Benjamin Cauchi,Kamil Adiloglu,Jörn Anemüller,Simon Doclo,Birger Kollmeier,Stefan Goetze +8 more
TL;DR: It is demonstrated that the proposed spectro-temporal features achieve a better recognition accuracy than MFCCs.
Journal ArticleDOI
Regularization for Partial Multichannel Equalization for Speech Dereverberation
TL;DR: A partial multichannel equalization technique based on MINT (P-MINT) is proposed which aims to shorten the RIR and an automatic non-intrusive procedure for determining the regularization parameter based on the L-curve is introduced.
Journal ArticleDOI
Combination of MVDR beamforming and single-channel spectral processing for enhancing noisy and reverberant speech
Benjamin Cauchi,Ina Kodrasi,Robert Rehr,Stephan Gerlach,Ante Jukic,Timo Gerkmann,Simon Doclo,Stefan Goetze +7 more
TL;DR: Experimental results show that the proposed system is effective in suppressing both reverberation and noise while improving the speech quality, and the achieved improvements are particularly significant in conditions with high reverberation times.
Journal ArticleDOI
Acoustic Monitoring and Localization for Social Care
TL;DR: The proposed system is able to reduce the false alarm rate compared to other existing and commercially available approaches that basically rely only on the acoustic level and explicitly distinguishes between the various acoustic events and provides information on the type of emergency that has taken place.
Proceedings ArticleDOI
Automatic acoustic siren detection in traffic noise by part-based models
TL;DR: Simulation results show that PBMs are a promising approach for acoustic event detection (AED) and a discriminative training approach in addition to standard generative training is implemented.