An Investigation on feature extraction and feature selection for power quality classification with high resolution and RMS data.. März 2021. [PUMA: An Investigation RMS and classification data. extraction feature for high on power quality resolution selection with]
Feature-based volumetric terrain generation and decoration. IEEE Transactions on Visualization and Computer Graphics, (25)2:1283--1296, 2019. [PUMA: (computer (mathematics), Computational GPU, Ptex, Rendering Splines Terrain, Three-dimensional Tools, algorithms, common complete computer curve-based curves, data data, diffusion, diffusion-based displays, extraction, feature feature-based features, field, fields, generation, graphics), graphics, height input interactive large-scale layers, manual mapping, modeling, modelling modelling, multiple offline primitives, prominent real-time rendering rendering, representation representations, scale sparse spline structure, structures, surface surface, terrain texture, three-dimensional toolset, two-dimensional vertical visualisation, volumetric workflow,]
Recognising realistic emotions and affect in speech: State of the art and lessons learnt from the first challenge. Speech Communication, (53)9:1062--1087, Elsevier, 2011. [PUMA: Feature emotion recognition, review selection, speech]
OpenEAR---introducing the Munich open-source emotion and affect recognition toolkit. 2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops, 1--6, 2009. [PUMA: Feature extraction openEAR, toolkits,]
Learning salient features for speech emotion recognition using convolutional neural networks. IEEE Transactions on Multimedia, (16)8:2203--2213, IEEE, 2014. [PUMA: CNN, DES, Emo-DB, MES, SAVEE, analysis, classification discriminative emotion feature learning, recognition, salient speech]
The Geneva minimalistic acoustic parameter set (GeMAPS) for voice research and affective computing. IEEE Transactions on Affective Computing, (7)2:190--202, IEEE, 2016. [PUMA: Feature GeMAPS, engineering]
Recent developments in openSMILE, the munich open-source multimedia feature extractor. Proceedings of the 21st ACM international conference on Multimedia, 835--838, 2013. [PUMA: Feature extraction openSMILE, toolkits,]
Adaptive and optimal classification of speech emotion recognition. 2008 Fourth International Conference on Natural Computation, (5):407--411, 2008. [PUMA: Algorithm, Feature Genetic SVM, extraction]
Deep learning for robust feature generation in audiovisual emotion recognition. Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on, 3687--3691, 2013. [PUMA: IEMOCAP, audiovisual belief deep feature learning, network, selection,]
Feature Subset Selection in Conditional Random Fields for Named Entity Recognition. In Galia Angelova, Kalina Bontcheva, Ruslan Mitkov, Nicolas Nicolov, und Nicolai Nikolov (Hrsg.), Proceedings of Recent Advances in Natural Language Processing (RANLP), 185-191, Borovets, Bulgaria, September 2009. [PUMA: crf feature myown ner selection] URL
Defining, recognizing and representing feature interactions in a feature-baseddata model: Defining, recognizing and representing feature interactions in a feature-baseddata model. --, 2011. [PUMA: Feature ISW data feature-based interactions, model]