@techreport{DuckhornWolffTschoepe, author = {Duckhorn, Frank and Wolff, Matthias and Tsch{\"o}pe, Constanze}, title = {Hidden Markov Model training using Finite State Machines}, publisher = {Technische Universit{\"a}t, Institut f{\"u}r Akustik und Sprachkommunikation}, address = {Dresden}, language = {de} } @misc{TschoepeWolffBorchers, author = {Tsch{\"o}pe, Constanze and Wolff, Matthias and Borchers, B.}, title = {Verfahren zur Bestimmung der Weichheit von Tissuepapier}, language = {de} } @misc{HentschelTschoepeHoffmannetal., author = {Hentschel, Dieter and Tsch{\"o}pe, Constanze and Hoffmann, R{\"u}diger and Eichner, Matthias and Wolff, Matthias}, title = {Vorrichtung und Verfahren zur Beurteilung einer G{\"u}teklasse eines zu pr{\"u}fenden Objekts}, language = {de} } @inproceedings{TschoepeJoneitDuckhornetal., author = {Tsch{\"o}pe, Constanze and Joneit, Dieter and Duckhorn, Frank and Strecha, Guntram and Hoffmann, R{\"u}diger and Wolff, Matthias}, title = {Voice control for measurement devices}, series = {AIA-DAGA 2013, proceedings of the International Conference on Acoustics , 18 - 21 March 2013 in Merano}, booktitle = {AIA-DAGA 2013, proceedings of the International Conference on Acoustics , 18 - 21 March 2013 in Merano}, publisher = {DEGA}, address = {Berlin}, language = {en} } @inproceedings{TschoepeWolff, author = {Tsch{\"o}pe, Constanze and Wolff, Matthias}, title = {Zahnradpr{\"u}fung mit statistischen Klassifikatoren}, series = {ZfP in Forschung, Entwicklung und Anwendung, Dresden, 6. - 8. Mai 2013, DGZfP-Jahrestagung 2013 Zerst{\"o}rungsfreie Materialpr{\"u}fung}, booktitle = {ZfP in Forschung, Entwicklung und Anwendung, Dresden, 6. - 8. Mai 2013, DGZfP-Jahrestagung 2013 Zerst{\"o}rungsfreie Materialpr{\"u}fung}, publisher = {DGZfP}, address = {Berlin}, language = {de} } @inproceedings{WolffKordonHusseinetal., author = {Wolff, Matthias and Kordon, Ulrich and Hussein, Hussein and Eichner, Matthias and Tsch{\"o}pe, Constanze and Hoffmann, R{\"u}diger}, title = {Auscultatory blood pressure measurement using HMMs}, series = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Honolulu, Hawaii, April 15-20, 2007}, booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Honolulu, Hawaii, April 15-20, 2007}, publisher = {IEEE}, doi = {10.1109/ICASSP.2007.366702}, pages = {I-405 -- I-408}, language = {en} } @misc{TschoepeWolffPetersenetal., author = {Tsch{\"o}pe, Constanze and Wolff, Matthias and Petersen, Christer and Buttgereit, David and Kr{\"u}ger, Hauke and Michel, Georg and G{\"o}ring, Elke}, title = {Universal Cognitive User Interface, 27. Konferenz Elektronische Sprachsignalverarbeitung 2016, Leipzig, 02.-04.03.2016 - Postersitzung 1}, address = {Leipzig}, language = {de} } @inproceedings{TschoepeDuckhornBluethgenetal., author = {Tsch{\"o}pe, Constanze and Duckhorn, Frank and Bl{\"u}thgen, Peter and Richter, Christian and Papsdorf, Gunther and Wolff, Matthias}, title = {Miniaturisiertes System zur intelligenten Signalverarbeitung}, series = {DGZFP-Jahrestagung 2017, Zerst{\"o}rungsfreie Materialpr{\"u}fung, Koblenz, 22.-24.05.2017, Kurzfassungen der Vortr{\"a}ge und Posterbeitr{\"a}ge}, booktitle = {DGZFP-Jahrestagung 2017, Zerst{\"o}rungsfreie Materialpr{\"u}fung, Koblenz, 22.-24.05.2017, Kurzfassungen der Vortr{\"a}ge und Posterbeitr{\"a}ge}, publisher = {DGZIP}, address = {Berlin}, pages = {S. 192}, language = {de} } @techreport{TschoepeDuckhornWolff, author = {Tsch{\"o}pe, Constanze and Duckhorn, Frank and Wolff, Matthias}, title = {Akustische Mustererkennung: Qualit{\"a}tskontrolle - Vorausschauende Instandhaltung - Zustands{\"u}berwachung}, publisher = {Fraunhofer-IKTS}, address = {Dresden}, pages = {6}, language = {de} } @misc{WolffRoemerTschoepeetal., author = {Wolff, Matthias and R{\"o}mer, Ronald and Tsch{\"o}pe, Constanze and Hentschel, Dieter}, title = {Verfahren und Vorrichtung zur Verhaltenssteuerung von Systemen}, language = {de} } @misc{TschoepeKraljevskiDuckhornetal., author = {Tsch{\"o}pe, Constanze and Kraljevski, Ivan and Duckhorn, Frank and Wolff, Matthias}, title = {Sprachtechnologie und akustische Mustererkennung in der medizinischen Anwendung}, series = {16. Landeskonferenz „Digitalisierung im Gesundheitswesen" 2021}, journal = {16. Landeskonferenz „Digitalisierung im Gesundheitswesen" 2021}, pages = {9}, language = {de} } @misc{WunderlichTschoepeDuckhorn, author = {Wunderlich, Christian and Tsch{\"o}pe, Constanze and Duckhorn, Frank}, title = {Advanced methods in NDE using machine learning approaches}, series = {44th Annual Review of Progress in Quantitative Nondestructive Evaluation, Provo, Utah, USA, 16-21 July 2017}, volume = {37}, journal = {44th Annual Review of Progress in Quantitative Nondestructive Evaluation, Provo, Utah, USA, 16-21 July 2017}, publisher = {AIP Publishing}, address = {College Park, Maryland}, issn = {0094-243X}, doi = {10.1063/1.5031519}, pages = {1 -- 8}, abstract = {Machine learning (ML) methods and algorithms have been applied recently with great success in quality control and predictive maintenance. Its goal to build new and/or leverage existing algorithms to learn from training data and give accurate predictions, or to find patterns, particularly with new and unseen similar data, fits perfectly to Non-Destructive Evaluation. The advantages of ML in NDE are obvious in such tasks as pattern recognition in acoustic signals or automated processing of images from X-ray, Ultrasonics or optical methods. Fraunhofer IKTS is using machine learning algorithms in acoustic signal analysis. The approach had been applied to such a variety of tasks in quality assessment. The principal approach is based on acoustic signal processing with a primary and secondary analysis step followed by a cognitive system to create model data. Already in the second analysis steps unsupervised learning algorithms as principal component analysis are used to simplify data structures. In the cognitive part of the software further unsupervised and supervised learning algorithms will be trained. Later the sensor signals from unknown samples can be recognized and classified automatically by the algorithms trained before. Recently the IKTS team was able to transfer the software for signal processing and pattern recognition to a small printed circuit board (PCB). Still, algorithms will be trained on an ordinary PC; however, trained algorithms run on the Digital Signal Processor and the FPGA chip. The identical approach will be used for pattern recognition in image analysis of OCT pictures. Some key requirements have to be fulfilled, however. A sufficiently large set of training data, a high signal-to-noise ratio, and an optimized and exact fixation of components are required. The automated testing can be done subsequently by the machine. By integrating the test data of many components along the value chain further optimization including lifetime and durability prediction based on big data becomes possible, even if components are used in different versions or configurations. This is the promise behind German Industry 4.0.}, language = {en} } @inproceedings{TschoepeHentschelWolffetal., author = {Tsch{\"o}pe, Constanze and Hentschel, Dieter and Wolff, Matthias and Eichner, Matthias and Hoffmann, R{\"u}diger}, title = {Classification of non-speech acoustic signals using structure models}, series = {Proceedings, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), May 17 - 21, 2004, Montreal, Canada, vol. 5}, volume = {5}, booktitle = {Proceedings, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), May 17 - 21, 2004, Montreal, Canada, vol. 5}, publisher = {IEEE Operations Center}, address = {Piscataway, NJ}, isbn = {0-7803-8484-9}, doi = {10.1109/ICASSP.2004.1327195}, pages = {653 -- 656}, abstract = {Non-speech acoustic signals are widely used as the input of systems for non-destructive testing. In this rapidly growing field, the signals have an increasing complexity leading to the fact that powerful models are required. Methods like DTW and HMM, which are established in speech recognition, have been successfully used but are not sufficient in all cases. We propose the application of generalized structured Markov graphs (SMG). We describe a task independent structure learning technique which automatically adapts the models to the structure of the test signals. We demonstrate that our solution outperforms hand-tuned HMM structures in terms of class discrimination by two case studies using data from real applications.}, language = {en} }