TY - JOUR A1 - Becker, Martin A1 - Lippel, Jens A1 - Stuhlsatz, André A1 - Zielke, Thomas T1 - Robust dimensionality reduction for data visualization with deep neural networks JF - Graphical Models Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-26904 SN - 1524-0703 VL - 108 PB - Elsevier ER - TY - CHAP A1 - Stuhlsatz, André A1 - Lippel, Jens A1 - Zielke, Thomas T1 - Feature Extraction for Simple Classification T2 - 2010 20th International Conference on Pattern Recognition, August 23 - 26, 2010 , Istanbul, Turkey Y1 - 2010 SN - 978-0-7695-4109-9 U6 - https://doi.org/10.1109/ICPR.2010.377 SN - 1051-4651 SP - 1525 EP - 1528 PB - IEEE ER - TY - CHAP A1 - Stuhlsatz, André A1 - Meyer, Christine A1 - Eyben, Florian A1 - Zielke, Thomas A1 - Meier, Gunter A1 - Schuller, Bjorn T1 - Deep neural networks for acoustic emotion recognition: Raising the benchmarks T2 - 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) N2 - 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. Y1 - 2011 U6 - https://doi.org/10.1109/ICASSP.2011.5947651 SP - 5688 EP - 5691 PB - IEEE ER - TY - JOUR A1 - Stuhlsatz, André A1 - Lippel, Jens A1 - Zielke, Thomas T1 - Feature extraction with deep neural networks by a generalized discriminant analysis JF - IEEE transactions on neural networks and learning systems N2 - We present an approach to feature extraction that is a generalization of the classical linear discriminant analysis (LDA) on the basis of deep neural networks (DNNs). As for LDA, discriminative features generated from independent Gaussian class conditionals are assumed. This modeling has the advantages that the intrinsic dimensionality of the feature space is bounded by the number of classes and that the optimal discriminant function is linear. Unfortunately, linear transformations are insufficient to extract optimal discriminative features from arbitrarily distributed raw measurements. The generalized discriminant analysis (GerDA) proposed in this paper uses nonlinear transformations that are learnt by DNNs in a semisupervised fashion. We show that the feature extraction based on our approach displays excellent performance on real-world recognition and detection tasks, such as handwritten digit recognition and face detection. In a series of experiments, we evaluate GerDA features with respect to dimensionality reduction, visualization, classification, and detection. Moreover, we show that GerDA DNNs can preprocess truly high-dimensional input data to low-dimensional representations that facilitate accurate predictions even if simple linear predictors or measures of similarity are used. Y1 - 2012 U6 - https://doi.org/10.1109/tnnls.2012.2183645 VL - 23 IS - 4 SP - 596 EP - 608 PB - IEEE ER - TY - CHAP A1 - Stuhlsatz, André A1 - Lippel, Jens A1 - Zielke, Thomas T1 - Discriminative feature extraction with Deep Neural Networks T2 - The 2010 International Joint Conference on Neural Networks (IJCNN), 18.07.2010 - 23.07.2010, Barcelona, Spain Y1 - 2010 UR - http://ieeexplore.ieee.org/document/5596329/ SN - 978-1-4244-6916-1 U6 - https://doi.org/10.1109/IJCNN.2010.5596329 SP - 1 EP - 8 PB - IEEE ER -