Prof. Dr.-Ing. André Stuhlsatz

​
Internationale Journal-Artikel

1. M. Becker, J. Lippel, A. Stuhlsatz, T. Zielke: „Robust Dimensionality Reduction for Data Visualization with Deep Neural Networks", Graphical Models 108:101060, March, 2020
   
2. A. Stuhlsatz, J. Lippel, and T. Zielke: „Feature Extraction With Deep Neural Networks by a Generalized Discriminant Analysis“, IEEE Transactions on Neural Networks and Learning Systems 23(4):596-608, April, 2012
   
3. D. Gaida, C. Wolf, C. Meyer, A. Stuhlsatz, J. Lippel, T. Bäck, M. Bongards ,S. McLoone: „State Estimation for Anaerobic Digesters using the ADM1”, Water Sci Technol (2012) 66 (5): 1088-1095, 2012
   
4. B. Schuller, B. Vlasenko, F. Eyben, M. Wöllmer, A. Stuhlsatz, A. Wendemuth, and G. Rigoll: „Cross-Corpus Acoustic Emotion Recognition: Variances and Strategies“, IEEE Transactions on Affective Computing, 2010
   
5. C. Wolf, D. Gaida, A. Stuhlsatz, S. McLoone, and M. Bongards: „Predicting Organic Acid Concentration from UV/vis Spectro Measurements – A Comparison of Machine Learning Techniques“, Life System Modeling and Intelligent Computing, Communications in Computer and Information Science, Volume 97, Part 5, 200-206, 2010

Internationale Konferenz-Artikel

1. A. Stuhlsatz, T. Wedell, M. Moors, S. Schulze: „A Smart Measuring System for Intelligent Data Acquisition in Steel Plants“, ESTAD, Düsseldorf, 2019
2. M. Becker, J. Lippel, A. Stuhlsatz: „Regularized Nonlinear Discriminant Analysis – An Approach to Robust Dimensionality Reduction for Data Visualization“, 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, Porto, Portugal,  2017
3. B. Schuller, B. Vlasenko, F. Eyben, M. Wöllmer, A. Stuhlsatz, A. Wendemuth, and G. Rigoll: „Cross-Corpus Acoustic Emotion Recognition: Variances and Strategies (Extended Abstract)“, In Proc. 6th biannual Conference on Affective Computing and Intelligent Interaction (ACII 2015), AAAC, IEEE, pp. 470–476. invited for the Special Session on Most Influential Articles in IEEE Transactions on Affective Computing, 2015
4. D. Gaida, C. Wolf, C. Meyer, A. Stuhlsatz, J. Lippel, T. Bäck, M. Bongards, and S. McLoone: “State Estimation for Anaerobic Digesters using the ADM1”,  In Proceedings of International IWA-Symposium on Anaerobic Digestion of Solid Waste and Energy Crops,  Vienna, Austria, 2011
5. A. Stuhlsatz, C. Meyer, F. Eyben, T. Zielke, G. Meier, and B. Schuller: „Deep Neural Networks for Acoustic Emotion Recognition: Raising the Benchmarks“, In Proceedings of the 2011 International Conference on Acoustic, Speech and Signal Processing (ICASSP), Prag, Czech Republic, 2011
6. A. Stuhlsatz, J. Lippel, and T. Zielke: „Discriminative Feature Extraction with Deep Neural Networks“, In Proceedings of the 2010 International Joint Conference on Neural Networks (IJCNN), Barcelona, Spain, 2010
7. A. Stuhlsatz, J. Lippel, and T. Zielke: „Feature Extraction for Simple Classification“, In Proceedings of the 20th International Conference on Pattern Recognition (ICPR), Istanbul, Turkey, 2010
8. C. Wolf, D. Gaida, A. Stuhlsatz, S. McLoone, and M. Bongards: „A Comparison of Performance of Advanced Pattern Recognition Methods for Organic Acid Prediction in Biogas Plants Using UV/vis Spectroscopic Online-Measurements“, In Proceedings of the 2010 International Conference on Life System Modeling and Simulation (LSMS), Wuxi, China, 2010
9. A. Stuhlsatz, H.-G. Meier, and A. Wendemuth: „Making the Lipschitz Classifier Practical via Semi-Infinite Programming“, In Proceedings of the 7th International Conference on Machine Learning and Applications (ICMLA), IEEE Computer Society, ISBN-978-0-7695-3495-4, San Diego CA, USA, 2008
10. A. Stuhlsatz, H.-G. Meier, and A. Wendemuth: „Maximum Margin Classification on Convex Euclidean Metric Spaces“, International Conference on Computer Recognition Systems, In Computer Recognition Systems 2, Vol. 45, Advances in Soft Computing, Springer Verlag, ISBN-13 978-3-540-75174-8, Wroclaw, Poland, 2007
11. A. Stuhlsatz: „Recognition of Ultrasonic Multi-Echo Sequences for Autonomous Symbolic Indoor Tracking“, In Proceedings of the 6th International Conference on Machine Learning and Applications (ICMLA), IEEE Computer Society, ISBN-978-0-7695-3069-7, Cincinnatti OH, USA, 2007
12. A. Stuhlsatz, H.-G. Meier, M. Katz, S. E. Krüger, and A. Wendemuth: „Support Vector Machines for Postprocessing of Speech Recognition Hypotheses“, In Proceedings of the International Conference on Telecommunications and Multimedia (TEMU), Heraklion (Crete), Greece, 2006

Nationale Konferenz-/Workshop-Artikel

1. A. Stuhlsatz, H.-G. Meier, and A. Wendemuth: „A Dual Formulation to the Lipschitz Classifier“, In Proceedings of the Reasearch Workshop on Emotion-, Speech- and Face Recognition with Advanced Classifiers, ISBN-978-3-940961-24-2, Magdeburg, Germany, 2008
2. D. Gaida, A. Stuhlsatz, and H.-G. Meier: „Fusion of Visual and Inertial Measurements for Pose Estimation“, In Proceedings of the Reasearch Workshop on Emotion-, Speech- and Face Recognition with Advanced Classifiers, ISBN-978-3-940961-24-2, Magdeburg, Germany, 2008
3. A. Stuhlsatz: „HSVM – A SVM Toolkit for Segmented Speech Data“, In Proceedings of Elektronische Sprachsignalverarbeitung (ESSV), Vol. 46, Studientexte zur Sprachkommunikation, TUDpress, ISSN-0940-6832, Cottbus, Germany, 2007
4. A. Stuhlsatz, H.-G. Meier, M. Katz, S. E. Krüger, and A. Wendemuth: „Classification of Speech Recognition Hypotheses with Support Vector Machines“, In Proceedings of the Speech Processing Workshop in connection with DAGM, ISBN-3-929757-59-1, 2003

Fachbuch

A. Stuhlsatz: „Hybride Spracherkennung – Eine HMM/SVM-Systemintegration“, VDM Verlag, ISBN-978-3-639-10062-4, 2008

Link zu Amazon

Dissertation

A. Stuhlsatz: „Machine Learning with Lipschitz Classifiers“, Dissertation, http://edoc.bibliothek.uni-halle.de/receive/HALCoRe_document_00008271, Otto-von-Guericke-Universität Magdeburg, 2010, Link zur Dissertationsschrift

Patente