@inproceedings{LippelBihlmeierStuhlsatz2025, author = {Lippel, Jens and Bihlmeier, Richard and Stuhlsatz, Andr{\´e}}, title = {A Computer Vision Approach to Fertilizer Detection and Classification}, series = {Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications}, booktitle = {Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications}, publisher = {SCITEPRESS - Science and Technology Publications}, doi = {10.5220/0013189300003912}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:due62-opus-54735}, pages = {563 -- 569}, year = {2025}, subject = {Maschinelles Lernen}, language = {en} } @inproceedings{SchullerVlasenkoEybenetal.2015, author = {Schuller, Bj{\"o}rn and Vlasenko, Bogdan and Eyben, Florian and W{\"o}llmer, Martin and Stuhlsatz, Andr{\´e} and Wendemuth, Andreas and Rigoll, Gerhard}, title = {Cross-Corpus Acoustic Emotion Recognition: Variances and Strategies (Extended Abstract)}, series = {In Proc. 6th biannual Conference on Affective Computing and Intelligent Interaction (ACII 2015), AAAC,}, booktitle = {In Proc. 6th biannual Conference on Affective Computing and Intelligent Interaction (ACII 2015), AAAC,}, year = {2015}, abstract = {As the recognition of emotion from speech has matured to a degree where it becomes applicable in real-life settings, it is time for a realistic view on obtainable performances. Most studies tend to overestimation in this respect: acted data is often used rather than spontaneous data, results are reported on pre-selected prototypical data, and true speaker disjunctive partitioning is still less common than simple cross-validation. A considerably more realistic impression can be gathered by inter-set evaluation: we therefore show results employing six standard databases in a cross-corpora evaluation experiment. To better cope with the observed high variances, different types of normalization are investigated. 1.8k individual evaluations in total indicate the crucial performance inferiority of inter- to intra-corpus testing.}, language = {en} } @article{BeckerLippelStuhlsatzetal.2020, author = {Becker, Martin and Lippel, Jens and Stuhlsatz, Andr{\´e} and Zielke, Thomas}, title = {Robust dimensionality reduction for data visualization with deep neural networks}, series = {Graphical Models}, volume = {108}, journal = {Graphical Models}, publisher = {Elsevier}, issn = {1524-0703}, doi = {10.1016/j.gmod.2020.101060}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:due62-opus-26904}, pages = {101060}, year = {2020}, language = {en} } @inproceedings{StuhlsatzLippelZielke2010, author = {Stuhlsatz, Andr{\´e} and Lippel, Jens and Zielke, Thomas}, title = {Feature Extraction for Simple Classification}, series = {2010 20th International Conference on Pattern Recognition, August 23 - 26, 2010 , Istanbul, Turkey}, booktitle = {2010 20th International Conference on Pattern Recognition, August 23 - 26, 2010 , Istanbul, Turkey}, publisher = {IEEE}, isbn = {978-0-7695-4109-9}, issn = {1051-4651}, doi = {10.1109/ICPR.2010.377}, pages = {1525 -- 1528}, year = {2010}, language = {en} } @inproceedings{StuhlsatzWedellMoorsetal.2019, author = {Stuhlsatz, Andr{\´e} and Wedell, Tobias and Moors, Mark and Schulze, Stephan}, title = {A smart measuring system for intelligent data acquisition in steel plants}, series = {METEC \& 4th ESTAD 2019, European Steel Technology and Application Day, CCD Congress Center D{\"u}sseldorf, 24-28 June 2019}, booktitle = {METEC \& 4th ESTAD 2019, European Steel Technology and Application Day, CCD Congress Center D{\"u}sseldorf, 24-28 June 2019}, publisher = {Steel Institute VDEh}, address = {D{\"u}sseldorf}, year = {2019}, abstract = {Sensing and acquiring reliable physical values are the fundamentals, not only for a predictive maintenance or quality assessment, but especially for big data analysis and sophisticated Industry 4.0 applications. In steel plants, physical values are distributed over the complete process chain of steel making while the environmental conditions are harsh with respect to high temperature, aggressive fluids, water, shock and dust. These conditions render the use of electronic devices focusing a consumer market impossible.The developed self-contained smart measuring system presented in this paper survives in harsh environments and is composed of small-sized modules providing miscellaneous functionalities. The high degree of modularity in hard-and software facilitates a cost-effective adaptation to many applications, like vibration monitoring, temperature logging or torque measurement. Different onboard measurement components are available yet, namely high sensitive bridge amplifiers for strain measurements, accelerometers, gyroscopes, orientation-, temperature- and humidity sensors as well as an unique system identification. A communication module enables wireless transmission of the acquired data via Bluetooth or NFC online. Moreover, different power supply features are supported by the power module: Ultra-low power modes for long-life battery use, recharging of lithium cells, and an inductive power supply for wireless power transfer for applications with moving or rotating components. The heart of the system is a powerful ARM based microcontroller which enables an intelligent analysis of the data in situ. This is especially important where data size and complexity is the relevant factor for example in area-wide sensor networks.}, language = {en} } @inproceedings{StuhlsatzMeyerEybenetal.2011, author = {Stuhlsatz, Andr{\´e} and Meyer, Christine and Eyben, Florian and Zielke, Thomas and Meier, Gunter and Schuller, Bjorn}, title = {Deep neural networks for acoustic emotion recognition: Raising the benchmarks}, series = {2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, booktitle = {2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, publisher = {IEEE}, doi = {10.1109/ICASSP.2011.5947651}, pages = {5688 -- 5691}, year = {2011}, abstract = {Deep Neural Networks (DNNs) denote multilayer artificial neural networks with more than one hidden layer and millions of free parameters. We propose a Generalized Discriminant Analysis (GerDA) based on DNNs to learn discriminative features of low dimension optimized with respect to a fast classification from a large set of acoustic features for emotion recognition. On nine frequently used emotional speech corpora, we compare the performance of GerDA features and their subsequent linear classification with previously reported benchmarks obtained using the same set of acoustic features classified by Support Vector Machines (SVMs). Our results impressively show that low-dimensional GerDA features capture hidden information from the acoustic features leading to a significantly raised unweighted average recall and considerably raised weighted average recall.}, language = {en} } @incollection{StuhlsatzMeierWendemuth2007, author = {Stuhlsatz, Andr{\´e} and Meier, Hans-G{\"u}nter and Wendemuth, Andreas}, title = {Maximum Margin Classification on Convex Euclidean Metric Spaces}, series = {Advances in Soft Computing}, volume = {AINSC,volume 45}, booktitle = {Advances in Soft Computing}, editor = {Kurzynski, Marek and Puchala, Edward and Wozniak, Michal and Zolnierek, Andrzej}, publisher = {Springer Nature}, address = {Berlin, Heidelberg}, isbn = {9783540751748}, issn = {1867-5662}, doi = {10.1007/978-3-540-75175-5_27}, pages = {216 -- 223}, year = {2007}, language = {en} } @inproceedings{StuhlsatzMeierWendemuth2008, author = {Stuhlsatz, Andr{\´e} and Meier, Hans-G{\"u}nter and Wendemuth, Andreas}, title = {Making the Lipschitz Classifier Practical via Semi-infinite Programming}, series = {2008 Seventh International Conference on Machine Learning and Applications, 11-13 December 2008, San Diego}, booktitle = {2008 Seventh International Conference on Machine Learning and Applications, 11-13 December 2008, San Diego}, publisher = {IEEE}, isbn = {978-0-7695-3495-4}, doi = {10.1109/ICMLA.2008.26}, pages = {40 -- 47}, year = {2008}, language = {en} } @article{SchullerVlasenkoEybenetal.2010, author = {Schuller, Bjorn and Vlasenko, Bogdan and Eyben, Florian and Wollmer, Martin and Stuhlsatz, Andr{\´e} and Wendemuth, Andreas and Rigoll, Gerhard}, title = {Cross-Corpus Acoustic Emotion Recognition: Variances and Strategies}, series = {IEEE Transactions on Affective Computing}, volume = {1}, journal = {IEEE Transactions on Affective Computing}, number = {2}, publisher = {IEEE}, doi = {10.1109/t-affc.2010.8}, pages = {119 -- 131}, year = {2010}, language = {en} } @inproceedings{Stuhlsatz2007, author = {Stuhlsatz, Andr{\´e}}, title = {Recognition of ultrasonic multi-echo sequences for autonomous symbolic indoor tracking}, series = {Sixth International Conference on Machine Learning and Applications (ICMLA 2007), 13-15 December 2007, Cincinnati}, booktitle = {Sixth International Conference on Machine Learning and Applications (ICMLA 2007), 13-15 December 2007, Cincinnati}, publisher = {IEEE}, isbn = {978-0-7695-3069-7}, doi = {10.1109/ICMLA.2007.30}, pages = {178 -- 185}, year = {2007}, language = {en} }