@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} } @inproceedings{WolffSchubertHoffmannetal., author = {Wolff, Matthias and Schubert, R. and Hoffmann, R{\"u}diger and Tsch{\"o}pe, Constanze and Schulze, E. and Neun{\"u}bel, H.}, title = {Experiments in Acoustic Structural Health Monitoring of Airplane Parts}, series = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2008), 30.3.-4.4.2008, Las Vegas, USA}, booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2008), 30.3.-4.4.2008, Las Vegas, USA}, publisher = {IEEE}, isbn = {978-1-4244-1483-3}, doi = {10.1109/ICASSP.2008.4518040}, pages = {2037 -- 2040}, language = {en} } @incollection{HoffmannEichnerKordonetal., author = {Hoffmann, R{\"u}diger and Eichner, Matthias and Kordon, Ulrich and Tsch{\"o}pe, Constanze and Wolff, Matthias}, title = {Anwendung von Spracherkennungsalgorithmen auf nichtsprachliche akustische Signale}, series = {Sprachsignalverarbeitung : Analyse und Anwendungen ; zum 65. Geburtstag von Klaus Fellbaum}, booktitle = {Sprachsignalverarbeitung : Analyse und Anwendungen ; zum 65. Geburtstag von Klaus Fellbaum}, editor = {Hentschel, Christian}, publisher = {TUDpress}, address = {Dresden}, isbn = {978-3-940046-02-4}, pages = {46 -- 57}, language = {de} } @inproceedings{StrechaWolffDuckhornetal., author = {Strecha, Guntram and Wolff, Matthias and Duckhorn, Frank and Wittenberg, S{\"o}ren and Tsch{\"o}pe, Constanze}, title = {The HMM synthesis algorithm of an embedded unified speech recognizer and synthesizer}, series = {Proceedings of the Annual Conference of the International Speech Communication Association 2009, Interspeech 2009, 6 - 10 September, 2009, Brighton, UK}, booktitle = {Proceedings of the Annual Conference of the International Speech Communication Association 2009, Interspeech 2009, 6 - 10 September, 2009, Brighton, UK}, publisher = {ISCA}, address = {Brighton}, pages = {1763 -- 1766}, language = {en} } @misc{PuschCherifFarooqetal., author = {Pusch, T. and Cherif, Chokri and Farooq, Aamir and Wittenberg, S{\"o}ren and Wolff, Matthias and Hoffmann, R{\"u}diger and Tsch{\"o}pe, Constanze}, title = {Fehlerfr{\"u}herkennung an Textilmaschinen mit Hilfe der K{\"o}rperschallanalyse}, series = {Melliand Textilberichte}, volume = {90}, journal = {Melliand Textilberichte}, number = {3}, issn = {0341-0781}, pages = {113 -- 115}, language = {de} } @inproceedings{WolffTschoepe, author = {Wolff, Matthias and Tsch{\"o}pe, Constanze}, title = {Pattern recognition for sensor signals}, series = {Proceedings of the IEEE Sensors Conference 2009, Christchurch, New Zealand, 25 - 28 October 2009}, booktitle = {Proceedings of the IEEE Sensors Conference 2009, Christchurch, New Zealand, 25 - 28 October 2009}, publisher = {IEEE}, address = {Piscataway, NJ}, isbn = {978-1-424-44548-6}, doi = {10.1109/ICSENS.2009.5398338}, pages = {665 -- 668}, language = {en} } @phdthesis{Wolff, author = {Wolff, Matthias}, title = {Akustische Mustererkennung}, publisher = {TUDpress}, address = {Dresden}, isbn = {978-3-942710-14-5}, pages = {XIV, 313}, language = {de} } @misc{LiKraljevskiMeyeretal., author = {Li, Huajian and Kraljevski, Ivan and Meyer, Paul and Tsch{\"o}pe, Constanze and Wolff, Matthias}, title = {YOLO-ICP : deep learning integrated pose estimation for bin-picking of multiple objects}, series = {2024 IEEE SENSORS, Proceedings, Kobe, Japan, 2024}, journal = {2024 IEEE SENSORS, Proceedings, Kobe, Japan, 2024}, publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, address = {Piscataway, New Jersey}, isbn = {979-8-3503-6351-7}, doi = {10.1109/SENSORS60989.2024.10784539}, pages = {1 -- 4}, abstract = {In this paper, we present a novel deep learning-integrated pipeline called YOLO-ICP that aims to estimate the six degree of freedom (6-DoF) pose of objects using RGB-D sensors and does not require pose labels to train deep learning networks. YOLO-ICP integrates a real-time object detection algorithm with a point cloud registration method to estimate the pose of multiple objects. We evaluated our approach by quantitatively comparing it with baseline models on the OccludedLINEMOD dataset. Experimental results illustrate that our approach outperforms baseline models in challenging scenarios with textureless and occluded objects. In particular, our pipeline shows superior performance when dealing with small and symmetric objects in terms of the ADD(-S) metric.}, language = {en} } @misc{SchmidtHildebrandKraljevskietal., author = {Schmidt, Ralph Rudi and Hildebrand, Jorg and Kraljevski, Ivan and Duckhorn, Frank and Tsch{\"o}pe, Constanze}, title = {A study for laser additive manufacturing quality and material classification using machine learning}, series = {2022 IEEE sensors}, journal = {2022 IEEE sensors}, publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, address = {Piscataway, New Jersey}, isbn = {978-1-6654-8464-0}, issn = {2168-9229}, doi = {10.1109/SENSORS52175.2022.9967311}, pages = {1 -- 4}, abstract = {This paper demonstrates the use of acoustic emissions (AEs) to monitor the quality, and material used, for the laser additive manufacturing (LAM) process with steel and copper wire. Layers of deposited material (steel or copper) were created using LAM. The quality of these layers was either good or unstable. The AEs were recorded using three sensors, one microphone, and two structure-borne sound probes. The recorded signals were processed and transformed using the fast Fourier method. Then models were trained with the processed data and evaluated using a fivefold cross-validation. Results show that it is possible to accurately classify the materials used during LAM (up to a balanced accuracy [BAcc] score of 0.99). Also, the process quality could be classified with a BAcc score of up to 0.81. Overall, the results are promising, but further research and data collection are necessary for a proper validation of our results.}, language = {en} } @misc{OpitzWunderlichBendjusetal., author = {Opitz, Joerg and Wunderlich, Christian and Bendjus, B. and Cikalova, U. and Wolf, C. and Naumann, S. and Lehmann, A. and Barth, M. and Duckhorn, Frank and K{\"o}hler, B. and Tsch{\"o}ke, K. and Windisch, T. and Tsch{\"o}pe, Constanze and Moritz, T. and Scheithauer, U.}, title = {Materialdiagnose und integrale Pr{\"u}fverfahren f{\"u}r keramische Bauteile}, series = {Keramische Zeitschrift}, volume = {68}, journal = {Keramische Zeitschrift}, number = {4-5}, publisher = {Springer Science and Business Media LLC}, address = {Berlin ; Heidelberg}, issn = {0023-0561}, doi = {10.1007/BF03400267}, pages = {249 -- 254}, abstract = {Hochleistungskeramiken findet man heute h{\"a}ufig als kritische Komponente in neuentwickelten Systemen f{\"u}r Zukunftsanwendungen. Die Zuverl{\"a}ssigkeit des gesamten Systems basiert hierbei oft auf der kritischen keramischen Komponente. F{\"u}r diese oft neuentwickelten keramischen Materialien werden neue Methoden f{\"u}r die Prozesssteuerung, Materialdiagnostik und Struktur{\"u}berwachung ben{\"o}tigt. In diesem Artikel werden drei f{\"u}r die Keramikcharakterisierung am Fraunhofer-Institut f{\"u}r Keramische Technologien und Systeme IKTS weiter entwickelte Technologien und Verfahren beschrieben und deren Einsatz anhand von Beispielen illustriert. Dazu werden die Laser-Speckle-Photometrie, die optische Koh{\"a}renztomographie und die Klanganalyse in Kombination mit einer entsprechenden akustischen Mustererkennung als leistungsf{\"a}hige Verfahren f{\"u}r die Materialdiagnostik im Bereich der keramischen Materialien vorgestellt.}, language = {de} } @misc{KraljevskiDuckhornSobeetal., author = {Kraljevski, Ivan and Duckhorn, Frank and Sobe, Daniel and Tsch{\"o}pe, Constanze and Wolff, Matthias}, title = {Speech-to-text in upper sorbian : current state}, series = {Elektronische Sprachsignalverarbeitung 2025 : Tagungsband der 36. Konferenz Halle/Saale, 5.-7. M{\"a}rz 2025}, journal = {Elektronische Sprachsignalverarbeitung 2025 : Tagungsband der 36. Konferenz Halle/Saale, 5.-7. M{\"a}rz 2025}, editor = {Grawunder, Sven}, publisher = {TUDpress}, address = {Dresden}, isbn = {978-3-95908-803-9}, issn = {0940-6832}, pages = {109 -- 116}, abstract = {This study presents recent advancements in Upper Sorbian Speech-to-Text (STT) technology. We provide an overview of the Sorbian languages, the available speech and language resources, and the development of an STT system based on a traditional approach, which includes acoustic, pronunciation, and language modeling. Due to the scarcity of resources for Sorbian languages, our approach leverages sub-word and word-class modeling techniques. The word-class modeling is based on Finite-State Transducer definitions, which are applicable to both offline text parsing and integration into the decoding graph of the STT system. Word-class parsing is performed on the speech corpus and utilized for language modeling with complete words, sub-word units, or both. Additionally, the same definitions can be applied to Named Entity Recognition during the post-processing of recognized transcriptions. This approach significantly reduces out-of-vocabulary words and enables greater customization of the recognizer for domain-specific applications. The system was implemented for the real-time transcription of church sermon broadcasts in Upper Sorbian. The domain-specific system achieved performance comparable to fine-tuned OpenAI Whisper models developed also by other initiatives while also providing a resource-efficient solution with semantically tagged recognition results.}, language = {en} } @misc{KraljevskiDuckhornSobeetal., author = {Kraljevski, Ivan and Duckhorn, Frank and Sobe, Daniel and Tsch{\"o}pe, Constanze and Wolff, Matthias}, title = {Preserving Language Heritage Through Speech Technology: The Case of Upper Sorbian}, series = {Speech and Computer, SPECOM 2024, Belgrade, Serbia, 25-28 November 2024}, journal = {Speech and Computer, SPECOM 2024, Belgrade, Serbia, 25-28 November 2024}, editor = {Karpov, Alexey and Delic, Vlado}, publisher = {Springer Nature Switzerland, Cham}, isbn = {978-3-031-77960-2}, doi = {10.1007/978-3-031-77961-9_1}, pages = {3 -- 22}, abstract = {The modern world is facing a crisis with the rapid disappearance of endangered languages, which poses a serious threat to global cultural diversity. Speech Technologies and Artificial Intelligence present promising opportunities to address this crisis by supporting the documentation, revitalization, and everyday use of these vulnerable languages. However, despite recent and remarkable advancements in speech technology, significant challenges persist, particularly for languages with very limited resources and unique linguistic features. This paper details the development of Upper Sorbian speech technologies, focusing on the creation of a practical Speech-to-Text (STT) system as a versatile tool for language preservation. The study explores the current state of Sorbian languages and underscores collaborative efforts with the Foundation for the Sorbian People. Through a series of pilot and successive projects, each phase has contributed to the steady advancement of speech recognition modules and supporting tools, improving their performance, effectiveness and practical usability.}, language = {en} } @misc{SaeltzerTschoepeWolff, author = {Saeltzer, Gerhard and Tsch{\"o}pe, Constanze and Wolff, Matthias}, title = {Vorrichtung und Verfahren zur Bestimmung eines medizinischen Gesundheitsparameters eines Probanden mittels Stimmanalyse}, pages = {31}, abstract = {Eine Vorrichtung zur Bestimmung eines Gesundheitsparameters eines Probanden mittels Stimmauswertung umfasst eine Verarbeitungseinrichtung, die ausgebildet ist, um eine digitalisierte Sprechprobe des Probanden basierend auf individuellen Modellparametern auszuwerten, um eine Messinformation zu erhalten, die innerhalb eines Toleranzbereichs auf einem Momentanwert des Gesundheitsparameters des Probanden basiert, wobei die individuellen Modellparameter einen funktionalen Zusammenhang zwischen der Sprechprobe oder von der Sprechprobe abgeleiteten Sprechmerkmalen und einem zugeordneten, momentanen Gesundheitsparameter angeben.}, language = {de} } @misc{WolffRoemerTschoepeetal., author = {Wolff, Matthias and R{\"o}mer, Ronald and Tsch{\"o}pe, Constanze and Hentschel, Dieter}, title = {Method and Device for Controlling the Behavior of Systems}, language = {en} } @inproceedings{TschoepeWolff, author = {Tsch{\"o}pe, Constanze and Wolff, Matthias}, title = {Automatic decision making in SHM using hidden Markov models}, series = {18th International Conference on Database and Expert Systems Applications (DEXA 2007), Regensburg, September 3-7, 2007}, booktitle = {18th International Conference on Database and Expert Systems Applications (DEXA 2007), Regensburg, September 3-7, 2007}, publisher = {IEEE}, doi = {10.1109/DEXA.2007.138}, pages = {307 -- 311}, language = {en} } @misc{TschoepeWolffHoffmann, author = {Tsch{\"o}pe, Constanze and Wolff, Matthias and Hoffmann, R{\"u}diger}, title = {Akustische Mustererkennung f{\"u}r die ZfP}, series = {MP Materials Testing}, volume = {51}, journal = {MP Materials Testing}, number = {10}, issn = {0025-5300}, pages = {701 -- 704}, language = {de} } @misc{LeithoffDilgerDuckhornetal., author = {Leithoff, Ruben and Dilger, Nikolas and Duckhorn, Frank and Blume, Stefan and Lembcke, Dario and Tsch{\"o}pe, Constanze and Herrmann, Christoph and Dr{\"o}der, Klaus}, title = {Inline monitoring of battery electrode lamination processes based on acoustic measurements}, series = {Batteries}, volume = {7}, journal = {Batteries}, number = {1}, publisher = {MDPI AG}, address = {Basel}, issn = {2313-0105}, doi = {10.3390/batteries7010019}, pages = {1 -- 21}, abstract = {Due to the energy transition and the growth of electromobility, the demand for lithium-ion batteries has increased in recent years. Great demands are being placed on the quality of battery cells and their electrochemical properties. Therefore, the understanding of interactions between products and processes and the implementation of quality management measures are essential factors that requires inline capable process monitoring. In battery cell lamination processes, a typical problem source of quality issues can be seen in missing or misaligned components (anodes, cathodes and separators). An automatic detection of missing or misaligned components, however, has not been established thus far. In this study, acoustic measurements to detect components in battery cell lamination were applied. Although the use of acoustic measurement methods for process monitoring has already proven its usefulness in various fields of application, it has not yet been applied to battery cell production. While laminating battery electrodes and separators, acoustic emissions were recorded. Signal analysis and machine learning techniques were used to acoustically distinguish the individual components that have been processed. This way, the detection of components with a balanced accuracy of up to 83\% was possible, proving the feasibility of the concept as an inline capable monitoring system.}, language = {en} } @misc{LieskeDuckhornTschoepeetal., author = {Lieske, Uwe and Duckhorn, Frank and Tsch{\"o}pe, Constanze and Harig, Paul and Pallmer, Matthias and R{\"o}der, Olaf and R{\"o}der, Elisabeth}, title = {Verfahren zur akustischen Detektion von Schadinsekten bei der Getreide- und Saatgutlagerung}, series = {DGZfP-Jahrestagung Zerst{\"o}rungsfreie Materialpr{\"u}fung 2017 : Kurzfassungen der Vortr{\"a}ge und Posterbeitr{\"a}ge : 22.-24. Mai 2017, Koblenz}, journal = {DGZfP-Jahrestagung Zerst{\"o}rungsfreie Materialpr{\"u}fung 2017 : Kurzfassungen der Vortr{\"a}ge und Posterbeitr{\"a}ge : 22.-24. Mai 2017, Koblenz}, publisher = {Deutsche Gesellschaft f{\"u}r Zerst{\"o}rungsfreie Pr{\"u}fung e.V.}, address = {Berlin}, pages = {2}, abstract = {Getreide bildet eine der wichtigsten Grundlagen f{\"u}r die menschliche und tierische Ern{\"a}hrung. Wie jedes andere pflanzliche Produkt, so ist jedoch Getreide auch Nahrungsquelle f{\"u}r zahlreiche Insekten, die w{\"a}hrend des Pflanzenwachstums und der Lagerung das Korn befallen k{\"o}nnen. J{\"a}hrlich werden etwa 10 \% der weltweiten Getreideernte durch Schadinsekten vernichtet. In der Praxis wird der Befall mit Schadinsekten im Rahmen von Stichproben durch visuelle Inspektion, Sieben oder Aufschwemmen der Getreidek{\"o}rner dokumentiert. Es werden auch einfache Handger{\"a}te mit Mikrofon und Verst{\"a}rker eingesetzt, um Sch{\"a}dlingsbefall durch Fressger{\"a}usche manuell zu erkennen. Eine fr{\"u}hzeitige Diagnose des Auftretens von Vorratssch{\"a}dlingen ist f{\"u}r die Einleitung von Gegenmaßnahmen und die Begrenzung des Schadens von großer Bedeutung. Das hier vorgestellte Verfahren bietet die M{\"o}glichkeit, die auftretenden Ger{\"a}usche automatisiert zu analysieren, und erm{\"o}glicht so eine schnelle und zuverl{\"a}ssige Erkennung von Schadinsekten. Im Rahmen der Untersuchungen wurden die auftretenden Ger{\"a}usche von Schadinsekten in den verschiedenen Entwicklungsstadien analysiert und eine geeignete Signalerfassungshardware f{\"u}r den Feldeinsatz entwickelt. Mit den implementierten Signalverarbeitungsalgorithmen der akustischen Mustererkennung erfolgt die automatisierte Auswertung und Klassifizierung der Sch{\"a}dlingsger{\"a}usche. Das Verfahren, die Hardware und die Messergebnisse werden vorgestellt.}, language = {de} } @incollection{KordonWolffTschoepe, author = {Kordon, Ulrich and Wolff, Matthias and Tsch{\"o}pe, Constanze}, title = {Mustererkennung f{\"u}r Sensorsignale}, editor = {Gerlach, Gerald}, publisher = {TUDpress}, address = {Dresden}, isbn = {978-3-941298-55-2}, pages = {69 -- 78}, language = {de} } @misc{TschoepeWolff, author = {Tsch{\"o}pe, Constanze and Wolff, Matthias}, title = {Statistical Classifiers for Structural Health Monitoring}, series = {IEEE sensors journal}, volume = {9}, journal = {IEEE sensors journal}, number = {11}, issn = {1530-437X}, doi = {10.1109/JSEN.2009.2019330}, pages = {1567 -- 1576}, language = {en} } @inproceedings{TschoepeWolff, author = {Tsch{\"o}pe, Constanze and Wolff, Matthias}, title = {Instrumentelle Bestimmung der Weichheit von Tissueprodukten}, series = {Forum Akustische Qualit{\"a}tssicherung 2010 der DGAQS, 03. und 04. November 2010 in Karlsruhe}, booktitle = {Forum Akustische Qualit{\"a}tssicherung 2010 der DGAQS, 03. und 04. November 2010 in Karlsruhe}, publisher = {DGaQs}, address = {Karlsruhe}, pages = {3-1 -- 3-3}, language = {de} } @inproceedings{TschoepeWolffHoffmann, author = {Tsch{\"o}pe, Constanze and Wolff, Matthias and Hoffmann, R{\"u}diger}, title = {Akustische Mustererkennung}, series = {DAGA 2011, 37. Jahrestagung f{\"u}r Akustik, D{\"u}sseldorf, 21. - 24. 3. 2011, Tagungsband "Fortschritte der Akustik"}, booktitle = {DAGA 2011, 37. Jahrestagung f{\"u}r Akustik, D{\"u}sseldorf, 21. - 24. 3. 2011, Tagungsband "Fortschritte der Akustik"}, editor = {Becker-Schweitzer, J{\"o}rg}, publisher = {Dt. Gesellschaft f{\"u}r Akustik}, address = {Berlin}, isbn = {978-3-939296-02-7}, pages = {345 -- 346}, language = {de} } @inproceedings{TschoepeWolff, author = {Tsch{\"o}pe, Constanze and Wolff, Matthias}, title = {Zur Formulierung von Hidden-Markov-Modellen als endliche Transduktoren}, series = {Elektronische Sprachsignalverarbeitung 2012, Tagungsband der 23. Konferenz, Cottbus, 29. - 31. August 2012}, booktitle = {Elektronische Sprachsignalverarbeitung 2012, Tagungsband der 23. Konferenz, Cottbus, 29. - 31. August 2012}, editor = {Wolff, Matthias}, publisher = {TUDpress}, address = {Dresden}, isbn = {978-3-942710-81-7}, pages = {120 -- 128}, language = {de} } @inproceedings{TschoepeJoneitDuckhornetal., author = {Tsch{\"o}pe, Constanze and Joneit, Dieter and Duckhorn, Frank and Hoffmann, R{\"u}diger and Strecha, Guntram and Wolff, Matthias}, title = {Sprachsteuerung f{\"u}r Mess- und Pr{\"u}fger{\"a}te}, series = {DGZfP-Jahrestagung 2011 Zerst{\"o}rungsfreie Materialpr{\"u}fung, 30. Mai - 1. Juni 2011, Bremen, Berichtsband}, booktitle = {DGZfP-Jahrestagung 2011 Zerst{\"o}rungsfreie Materialpr{\"u}fung, 30. Mai - 1. Juni 2011, Bremen, Berichtsband}, publisher = {DGZfP}, address = {Berlin}, isbn = {978-3-940283-33-7}, pages = {6}, language = {de} } @inproceedings{WolffTschoepeRoemeretal., author = {Wolff, Matthias and Tsch{\"o}pe, Constanze and R{\"o}mer, Ronald and Wirsching, G{\"u}nther}, title = {Subsymbol-Symbol-Transduktoren}, series = {Elektronische Sprachsignalverarbeitung 2013, Tagungsband, Bielefeld, 2013}, booktitle = {Elektronische Sprachsignalverarbeitung 2013, Tagungsband, Bielefeld, 2013}, editor = {Wagner, Petra}, publisher = {TUDpress}, address = {Dresden}, isbn = {978-3-94431-03-4}, pages = {197 -- 204}, language = {de} } @inproceedings{TschoepeWolffHoffmann, author = {Tsch{\"o}pe, Constanze and Wolff, Matthias and Hoffmann, R{\"u}diger}, title = {Anwendungen der akustischen Mustererkennung}, series = {Elektronische Sprachsignalverarbeitung 2013, Tagungsband, Bielefeld, 2013}, booktitle = {Elektronische Sprachsignalverarbeitung 2013, Tagungsband, Bielefeld, 2013}, editor = {Wagner, Petra}, publisher = {TUDpress}, address = {Dresden}, isbn = {978-3-94431-03-4}, pages = {205 -- 210}, language = {de} } @inproceedings{TschoepeWolffDuckhorn, author = {Tsch{\"o}pe, Constanze and Wolff, Matthias and Duckhorn, Frank}, title = {Zustands{\"u}berwachung von Magnetventilen anhand der Schaltger{\"a}usche}, series = {ZfP in Forschung, Entwicklung und Anwendung, Potsdam, 26. - 28. Mai 2014, DGZfP-Jahrestagung 2014}, booktitle = {ZfP in Forschung, Entwicklung und Anwendung, Potsdam, 26. - 28. Mai 2014, DGZfP-Jahrestagung 2014}, publisher = {DGZfP}, address = {Berlin}, isbn = {978-3-940283-61-0}, language = {de} } @incollection{WolffTschoepeRoemer, author = {Wolff, Matthias and Tsch{\"o}pe, Constanze and R{\"o}mer, Ronald}, title = {Quo vadis, UASR?}, series = {Systemtheorie Signalverarbeitung Sprachtechnologie}, booktitle = {Systemtheorie Signalverarbeitung Sprachtechnologie}, editor = {Mehnert, Dieter and Kordon, Ulrich and Wolff, Matthias}, publisher = {TUDpress}, address = {Dresden}, isbn = {978-3-944331-19-5}, pages = {276 -- 285}, language = {de} } @inproceedings{TschoepeWolff, author = {Tsch{\"o}pe, Constanze and Wolff, Matthias}, title = {Processing and evaluation of gear data using statistical classifiers}, series = {Proceedings of the 6th European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS 2012), Vienna, Sep. 2012}, booktitle = {Proceedings of the 6th European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS 2012), Vienna, Sep. 2012}, address = {Vienna}, isbn = {978-395-02481-9-7}, language = {en} } @misc{KraljevskiDuckhornJuetal., author = {Kraljevski, Ivan and Duckhorn, Frank and Ju, Yong Chul and Tsch{\"o}pe, Constanze and Richter, Christian and Wolff, Matthias}, title = {Acoustic Resonance Recognition of Coins}, series = {2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), 25-28 May 2020, Dubrovnik, Croatia}, journal = {2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), 25-28 May 2020, Dubrovnik, Croatia}, publisher = {IEEE Xplore}, isbn = {978-1-7281-4460-3}, doi = {10.1109/I2MTC43012.2020.9129256}, pages = {6}, abstract = {In this study, we compare different machine learning approaches applied to acoustic resonance recognition of coins. Euro-cents and Euro-coins were classified by the sound emerging when throwing the coins onto a hard surface.The used dataset is a representative example of a small data which was collected in carefully prepared experiments.Due to the small number of coin specimens and the count of the collected observations, it was interesting to see whether deep learning methods can achieve similarly or maybe even better classification performances compared with more traditional methods.The results of the multi-class prediction of coin denominations are presented and compared in terms of balanced accuracy and Matthews Correlation Coefficient metrics. The feature analysis methods combined with the employed classifiers achieved acceptable results, despite the relatively small dataset.}, language = {en} } @misc{KraljevskiDuckhornJuetal., author = {Kraljevski, Ivan and Duckhorn, Frank and Ju, Yong Chul and Tsch{\"o}pe, Constanze and Wolff, Matthias}, title = {Acoustic Resonance Testing of Glass IV Bottles}, series = {Artificial Intelligence Applications and Innovations : 16th IFIP WG 12.5 International Conference, AIAI 2020, Neos Marmaras, Greece, June 5-7, 2020, Proceedings, Part II}, volume = {Cham}, journal = {Artificial Intelligence Applications and Innovations : 16th IFIP WG 12.5 International Conference, AIAI 2020, Neos Marmaras, Greece, June 5-7, 2020, Proceedings, Part II}, editor = {Maglogiannis, Ilias and Iliadis, Lazaros S. and Pimenidis, Elias}, publisher = {Springer International Publishing}, isbn = {978-3-030-49186-4}, issn = {1868-4238}, doi = {10.1007/978-3-030-49186-4_17}, pages = {195 -- 206}, abstract = {In this paper, acoustic resonance testing on glass intravenous (IV) bottles is presented. Different machine learning methods were applied to distinguish acoustic observations of bottles with defects from the intact ones. Due to the very limited amount of available specimens, the question arises whether the deep learning methods can achieve similar or even better detection performance compared with traditional methods.}, language = {en} } @inproceedings{TschoepeDuckhornHuberetal., author = {Tsch{\"o}pe, Constanze and Duckhorn, Frank and Huber, Markus and Meyer, Werner and Wolff, Matthias}, title = {A Cognitive User Interface for a Multi-Modal Human-Machine Interaction}, series = {Speech and computer : 20th International Conference, SPECOM 2018, Leipzig, Germany, September 18-22, 2018, proceedings}, booktitle = {Speech and computer : 20th International Conference, SPECOM 2018, Leipzig, Germany, September 18-22, 2018, proceedings}, editor = {Karpov, Alexey and Jokisch, Oliver and Potapova, Rodmonga}, publisher = {Springer International Publishing}, address = {Cham}, isbn = {978-3-319-99578-6}, doi = {10.1007/978-3-319-99579-3}, pages = {707 -- 717}, language = {en} } @misc{DuckhornHuberMeyeretal., author = {Duckhorn, Frank and Huber, Markus and Meyer, Werner and Jokisch, Oliver and Tsch{\"o}pe, Constanze and Wolff, Matthias}, title = {Towards an Autarkic Embedded Cognitive User Interface}, series = {Proceedings Interspeech 2017, 20-24 August 2017, Stockholm}, journal = {Proceedings Interspeech 2017, 20-24 August 2017, Stockholm}, editor = {Lacerda, Francisco}, publisher = {ISCA}, doi = {10.21437/Interspeech.2017}, pages = {3435 -- 3436}, abstract = {ucuikt2015}, language = {en} } @inproceedings{TschoepeDuckhornRichteretal., author = {Tsch{\"o}pe, Constanze and Duckhorn, Frank and Richter, Christian and Bl{\"u}thgen, Peter and Wolff, Matthias}, title = {Intelligent Signal Processing on a Miniaturized Hardware Module}, series = {IEEE SENSORS Proceedings, Glasgow, Scotland, UK, Oct. 29 - Nov. 1, 2017}, booktitle = {IEEE SENSORS Proceedings, Glasgow, Scotland, UK, Oct. 29 - Nov. 1, 2017}, publisher = {IEEE}, address = {Piscataway, NJ}, isbn = {978-1-5090-1012-7}, doi = {10.1109/ICSENS.2017.8234023}, language = {en} } @inproceedings{TschoepeWolff, author = {Tsch{\"o}pe, Constanze and Wolff, Matthias}, title = {Mustererkennung in der technischen Diagnose}, series = {Tagungsband der 12. Tagung Technische Diagnostik 2016, 20.-21.10.2016, Hochschule Merseburg}, booktitle = {Tagungsband der 12. Tagung Technische Diagnostik 2016, 20.-21.10.2016, Hochschule Merseburg}, publisher = {Hochschule Merseburg}, address = {Merseburg}, isbn = {978-3-942703-64-2}, url = {http://nbn-resolving.de/urn:nbn:de:gbv:3:2-68977}, pages = {124 -- 125}, language = {de} } @inproceedings{TschoepeDuckhornRichteretal., author = {Tsch{\"o}pe, Constanze and Duckhorn, Frank and Richter, Christian and Bl{\"u}thgen, Peter and Wolff, Matthias}, title = {An embedded system for acoustic pattern recognition}, series = {IEEE SENSORS Proceedings, Glasgow, Scotland, UK, Oct. 29 - Nov. 1, 2017}, booktitle = {IEEE SENSORS Proceedings, Glasgow, Scotland, UK, Oct. 29 - Nov. 1, 2017}, publisher = {IEEE}, address = {Piscataway, NJ}, isbn = {978-1-5090-1012-7}, doi = {10.1109/ICSENS.2017.8234380}, pages = {3}, language = {en} } @inproceedings{TschoepeWolffSaeltzer, author = {Tsch{\"o}pe, Constanze and Wolff, Matthias and Saeltzer, G.}, title = {Estimating blood sugar from voice samples : a preliminary study}, series = {2015 International Conference on Computational Science and Computational Intelligence (CSCI 2015), Las Vegas, December 7-9, 2015}, booktitle = {2015 International Conference on Computational Science and Computational Intelligence (CSCI 2015), Las Vegas, December 7-9, 2015}, publisher = {IEEE}, doi = {10.1109/CSCI.2015.184}, pages = {804 -- 805}, language = {en} } @misc{TschoepeMuehleJuetal., author = {Tsch{\"o}pe, Constanze and M{\"u}hle, Maximilian and Ju, Yong Chul and Kraljevski, Ivan and Wolff, Matthias}, title = {K{\"u}nstliche Intelligenz in der ZfP - Welchen Beitrag kann KI in der ZfP leisten?}, series = {DGZfP-Jahrestagung 2021, 10.-11. Mai}, journal = {DGZfP-Jahrestagung 2021, 10.-11. Mai}, isbn = {978-3-947971-18-3}, pages = {17}, abstract = {K{\"u}nstliche Intelligenz zieht derzeit in alle Bereiche der Gesellschaft und des Lebens ein. Aber welchen Stellenwert hat sie momentan auf dem Gebiet der zerst{\"o}rungsfreien Pr{\"u}fung? Was kann KI leisten? Welche Herausforderungen m{\"u}ssen erfolgreich bew{\"a}ltigt werden? Gibt es das eine KI-Verfahren, welches prinzipiell f{\"u}r ZfP geeignet ist? Bei der Bauteil- und Materialpr{\"u}fung w{\"a}hrend und unmittelbar nach der Herstellung, der {\"U}berwachung von Verschleißteilen in Maschinen und Anlagen oder der Schadensdetektion an Bauteilen und Komponenten liefern ZfP-Verfahren Daten, die bewertet werden m{\"u}ssen. Obwohl inzwischen sehr leistungsf{\"a}hige Toolkits verf{\"u}gbar sind, erfordert der optimale Einsatz der KI f{\"u}r ein ZfP-Verfahren oftmals mehr. Die meisten Kunden m{\"o}chten nicht nur eine L{\"o}sung ihres Problems; sie wollen verstehen, warum die KI so und nicht anders entschieden hat, warum der Klassifikator das Bauteil einer bestimmten Klasse (z. B. gut/schlecht oder neuwertig/verschlissen/defekt) zugewiesen hat. Abh{\"a}ngig von der Klassifikationsaufgabe sowie der Art und der Anzahl der vorliegenden Daten kann ein geeignetes Verfahren bestimmt werden. Mit Methoden des maschinellen Lernens werden Modelle gebildet, welche die Basis f{\"u}r die KI-Verfahren zur Klassifikation bilden. Der Beitrag liefert einen {\"U}berblick {\"u}ber KI-Verfahren und deren Anwendungen in der zerst{\"o}rungsfreien Pr{\"u}fung. Zahlreiche Beispiele und Ergebnisse werden vorgestellt, um die Mannigfaltigkeit des Einsatzes in der ZfP und der bestehenden M{\"o}glichkeiten zu demonstrieren.}, language = {de} } @misc{KraljevskiRjelkaDuckhornetal., author = {Kraljevski, Ivan and Rjelka, Marek and Duckhorn, Frank and Tsch{\"o}pe, Constanze and Wolff, Matthias}, title = {Cross-Lingual Acoustic Modeling in Upper Sorbian - Preliminary Study}, series = {Elektronische Sprachsignalverarbeitung 2021 : Tagungsband der 32. Konferenz Berlin, 3.-5. M{\"a}rz 2021}, journal = {Elektronische Sprachsignalverarbeitung 2021 : Tagungsband der 32. Konferenz Berlin, 3.-5. M{\"a}rz 2021}, editor = {Hillmann, Stefan and Weiss, Benjamin and Michael, Thilo and M{\"o}ller, Sebastian}, publisher = {TUDpress}, address = {Dresden}, isbn = {978-3-959082-27-3}, issn = {0940-6832}, pages = {43 -- 50}, abstract = {In this paper, we present a preliminary study for acoustic modeling in Upper Sorbian, where a model of German was used in cross-lingual transfer learning. At first, we define the grapheme and phoneme inventories and map the target phonemes from the most similar German source equivalents. Phonetically balanced sentences for the recording prompts were selected from a combination of general and domain-specific textual data. The speech corpora with a total duration of around 11 hours was collected in controlled recording sessions involving an equal number of females, males, and children. The baseline acoustic model was employed to force-align the speech corpora given the knowledge-based phoneme mappings. How well the mappings were, was evaluated by the phoneme confusions in free-phoneme recognition. The new derived data-driven model with a reduced phoneme set was included in the adaptation and evaluation along with the baseline acoustic model. The model adaptation performance was cross-validated with the "Leave One Group Out" strategy. We observed major improvements in phoneme error rates after adaptation for the knowledge-based and data-driven phoneme mappings. The study confirmed the feasibility of transfer learning for acoustic model adaptation in the case of Upper Sorbian, at the same time demonstrating practical usability with a small vocabulary speech recognition application (Smart Lamp).}, language = {de} } @misc{KraljevskiDuckhornTschoepeetal., author = {Kraljevski, Ivan and Duckhorn, Frank and Tsch{\"o}pe, Constanze and Wolff, Matthias}, title = {Machine Learning for Anomaly Assessment in Sensor Networks for NDT in Aerospace}, series = {IEEE Sensors Journal}, volume = {21}, journal = {IEEE Sensors Journal}, number = {9}, issn = {1558-1748}, doi = {10.1109/JSEN.2021.3062941}, pages = {11000 -- 11008}, abstract = {We investigated and compared various algorithms in machine learning for anomaly assessment with different feature analyses on ultrasonic signals recorded by sensor networks. The following methods were used and compared in anomaly detection modeling: hidden Markov models (HMM), support vector machines (SVM), isolation forest (IF), and reconstruction autoencoders (AEC). They were trained exclusively on sensor signals of the intact state of structures commonly used in various industries, like aerospace and automotive. The signals obtained on artificially introduced damage states were used for performance evaluation. Anomaly assessment was evaluated and compared using various classifiers and feature analysis methods. We introduced novel methodologies for two processes. The first was the dataset preparation with anomalies. The second was the detection and damage severity assessment utilizing the intact object state exclusively. The experiments proved that robust anomaly detection is practically feasible. We were able to train accurate classifiers which had a considerable safety margin. Precise quantitative analysis of damage severity will also be possible when calibration data become available during exploitation or by using expert knowledge.}, language = {en} } @misc{KraljevskiBissiriDuckhornetal., author = {Kraljevski, Ivan and Bissiri, Maria Paola and Duckhorn, Frank and Tsch{\"o}pe, Constanze and Wolff, Matthias}, title = {Glottal Stops in Upper Sorbian: A Data-Driven Approach}, series = {Proc. Interspeech 2021, 30 August - 3 September, 2021, Brno, Czechia}, journal = {Proc. Interspeech 2021, 30 August - 3 September, 2021, Brno, Czechia}, doi = {10.21437/Interspeech.2021-1101}, pages = {1001 -- 1005}, abstract = {We present a data-driven approach for the quantitative analysis of glottal stops before word-initial vowels in Upper Sorbian, a West Slavic minority language spoken in Germany. Glottal stops are word-boundary markers and their detection can improve the performance of automatic speech recognition and speech synthesis systems. We employed cross-language transfer using an acoustic model in German to develop a forced-alignment method for the phonetic segmentation of a read-speech corpus in Upper Sorbian. The missing phonemic units were created by combining the existing phoneme models. In the forced-alignment procedure, the glottal stops were considered optional in front of word-initial vowels. To investigate the influence of speaker type (males, females, and children) and vowel on the occurrence of glottal stops, binomial regression analysis with a generalized linear mixed model was performed. Results show that children glottalize word-initial vowels more frequently than adults, and that glottal stop occurrences are influenced by vowel quality.}, language = {en} } @misc{KraljevskiDuckhornBarthetal., author = {Kraljevski, Ivan and Duckhorn, Frank and Barth, Martin and Tsch{\"o}pe, Constanze and Schubert, Frank and Wolff, Matthias}, title = {Autoencoder-based Ultrasonic NDT of Adhesive Bonds}, series = {IEEE SENSORS 2021, Conference Proceedings, Oct 31- Nov 4, Sydney, Australia}, journal = {IEEE SENSORS 2021, Conference Proceedings, Oct 31- Nov 4, Sydney, Australia}, publisher = {IEEE}, isbn = {978-1-7281-9501-8}, doi = {10.1109/SENSORS47087.2021.9639864}, pages = {4}, abstract = {We present an approach for ultrasonic non-destructive testing of adhesive bonding employing unsupervised machine learning with autoencoders.The models are trained exclusively on the features derived from pulse-echo ultrasonic signals on a specimen with good adhesive bonding and tested on another specimen with artificially added defects.The resulting pseudo-probabilities indicating anomalies are visualized and presented along to the C-scan of the same specimen. As a result, we achieved improved representation of the defects, allowing their automatic and reliable detection.}, language = {en} } @misc{KraljevskiDuckhornTschoepeetal., author = {Kraljevski, Ivan and Duckhorn, Frank and Tsch{\"o}pe, Constanze and Wolff, Matthias}, title = {Convolutional Autoencoders for Health Indicators Extraction in Piezoelectric Sensors}, series = {2020 IEEE Sensors, 25-28 Oct. 2020, Rotterdam, Netherlands,}, journal = {2020 IEEE Sensors, 25-28 Oct. 2020, Rotterdam, Netherlands,}, address = {Rotterdam, Netherlands}, isbn = {978-1-7281-6801-2}, doi = {10.1109/SENSORS47125.2020.9323023}, pages = {1 -- 4}, abstract = {We present a method for extracting health indicators from piezoelectric sensors applied in the case of microfluidic valves. Convolutional autoencoders were used to train a model on the normal operating conditions and tested on signals of different valves. The results of the model performance evaluation, as well as, the qualitative presentation of the indicator plots for each tested component, showed that the used approach is capable of detecting features that correspond to increasing component degradation. The extracted health indicators are the prerequisite and input for reliable remaining useful life prediction.}, language = {en} } @misc{KlimczakKuscheTschoepeetal., author = {Klimczak, Peter and Kusche, Isabel and Tsch{\"o}pe, Constanze and Wolff, Matthias}, title = {Menschliche und maschinelle Entscheidungsrationalit{\"a}t - Zur Kontrolle und Akzeptanz K{\"u}nstlicher Intelligenz}, series = {Zeitschrift f{\"u}r Medienwissenschaft 21 - K{\"u}nstliche Intelligenz}, journal = {Zeitschrift f{\"u}r Medienwissenschaft 21 - K{\"u}nstliche Intelligenz}, number = {2}, isbn = {978-3-8376-4468-5}, issn = {1869-1722}, doi = {/10.25969/mediarep/12631}, pages = {39 -- 45}, language = {de} } @incollection{KraljevskiTschoepeWolff, author = {Kraljevski, Ivan and Tsch{\"o}pe, Constanze and Wolff, Matthias}, title = {Limits and Prospects of Big Data and Small Data Approaches in AI Applications}, series = {AI - Limits and Prospects of Artificial Intelligence}, booktitle = {AI - Limits and Prospects of Artificial Intelligence}, editor = {Klimczak, Peter and Petersen, Christer}, publisher = {transcript Verlag}, address = {Bielefeld}, isbn = {978-3-8376-5732-6}, doi = {10.14361/9783839457320-006}, pages = {115 -- 142}, abstract = {The renaissance of artificial intelligence (AI) in the last decade can be credited to several factors, but chief among these is the ever-increasing availability and miniaturization of computational resources. This process has contributed to the rise of ubiquitous computing via popularizing smart devices and the Internet of Things in everyday life. In turn, this has resulted in the generation of increasingly enormous amounts of data. The tech giants are harvesting and storing data on their clients' behavior and, at the same time, introducing concerns about data privacy and protection. Suddenly, such an abundance of data and computing power, which was unimaginable a few decades ago, has caused a revival of old and the invention of new machine learning paradigms, like Deep Learning. Artificial intelligence has undergone a technological breakthrough in various fields, achieving better than human performance in many areas (such as vision, board games etc.). More complex tasks require more sophisticated algorithms that need more and more data. It has often been said that data is becoming a resource that is "more valuable than oil"; however, not all data is equally available and obtainable. Big data can be described by using the "four Vs"; data with immense velocity, volume, variety, and low veracity. In contrast, small data do not possess any of those qualities; they are limited in size and nature and are observed or produced in a controlled manner. Big data, along with powerful computing and storage resources, allow "black box" AI algorithms for various problems previously deemed unsolvable. One could create AI applications even without the underlying expert knowledge, assuming there are enough data and the right tools available (e.g. end-to-end speech recognition and generation, image and object recognition). There are numerous fields in science, industry and everyday life where AI has vast potential. However, due to the lack of big data, application is not straightforward or even possible. A good example is AI in medicine, where an AI system is intended to assist physicians in diagnosing and treating rare or previously never observed conditions, and there is no or an insufficient amount of data for reliable AI deployment. Both big and small data concepts have limitations and prospects for different fields of application. This paper will try to identify and present them by giving real-world examples in various AI fields.}, language = {en} } @misc{UhligAlkhasliSchubertetal., author = {Uhlig, Sebastian and Alkhasli, Ilkin and Schubert, Frank and Tsch{\"o}pe, Constanze and Wolff, Matthias}, title = {A Review of Synthetic and Augmented Training Data for Machine Learning in Ultrasonic Non-Destructive Evaluation}, series = {Ultrasonics}, journal = {Ultrasonics}, number = {134}, issn = {1874-9968}, doi = {10.1016/j.ultras.2023.107041}, abstract = {Ultrasonic Testing (UT) has seen increasing application of machine learning (ML) in recent years, promoting higher-level automation and decision-making in flaw detection and classification. Building a generalized training dataset to apply ML in non-destructive evaluation (NDE), and thus UT, is exceptionally difficult since data on pristine and representative flawed specimens are needed. Yet, in most UT test cases flawed specimen data is inherently rare making data coverage the leading problem when applying ML. Common data augmentation (DA) strategies offer limited solutions as they don't increase the dataset variance, which can lead to overfitting of the training data. The virtual defect method and the recent application of generative adversarial neural networks (GANs) in UT are sophisticated DA methods targeting to solve this problem. On the other hand, well-established research in modeling ultrasonic wave propagations allows for the generation of synthetic UT training data. In this context, we present a first thematic review to summarize the progress of the last decades on synthetic and augmented UT training data in NDE. Additionally, an overview of methods for synthetic UT data generation and augmentation is presented. Among numerical methods such as finite element, finite difference, and elastodynamic finite integration methods, semi-analytical methods such as general point source synthesis, superposition of Gaussian beams, and the pencil method as well as other UT modeling software are presented and discussed. Likewise, existing DA methods for one- and multidimensional UT data, feature space augmentation, and GANs for augmentation are presented and discussed. The paper closes with an in-detail discussion of the advantages and limitations of existing methods for both synthetic UT training data generation and DA of UT data to aid the decision-making of the reader for the application to specific test cases.}, language = {en} } @misc{MaierKuhnDuckhornetal., author = {Maier, Isidor Konrad and Kuhn, Johannes Ferdinand Joachim and Duckhorn, Frank and Kraljevski, Ivan and Sobe, Daniel and Wolff, Matthias and Tsch{\"o}pe, Constanze}, title = {Word Class Based Language Modeling: A Case of Upper Sorbian}, series = {Proceedings of The Workshop on Resources and Technologies for Indigenous, Endangered and Lesser-resourced Languages in Eurasia within the 13th Language Resources and Evaluation Conference, Marseille, France}, journal = {Proceedings of The Workshop on Resources and Technologies for Indigenous, Endangered and Lesser-resourced Languages in Eurasia within the 13th Language Resources and Evaluation Conference, Marseille, France}, publisher = {European Language Resources Association}, isbn = {978-2-493814-07-4}, pages = {28 -- 35}, abstract = {In this paper we show how word class based language modeling can support the integration of a small language in modern applications of speech technology. The methods described in this paper can be applied for any language. We demonstrate the methods on Upper Sorbian. The word classes model the semantic expressions of numerals, date and time of day. The implementation of the created grammars was realized in the form of finite-state-transducers (FSTs) and minimalists grammars (MGs). We practically demonstrate the usage of the FSTs in a simple smart-home speech application, that is able to set wake-up alarms and appointments expressed in a variety of spontaneous and natural sentences. While the created MGs are not integrated in an application for practical use yet, they provide evidence that MGs could potentially work more efficient than FSTs in built-on applications. In particular, MGs can work with a significantly smaller lexicon size, since their more complex structure lets them generate more expressions with less items, while still avoiding wrong expressions.}, language = {en} } @misc{KraljevskiJuIvanovetal., author = {Kraljevski, Ivan and Ju, Yong Chul and Ivanov, Dmitrij and Tsch{\"o}pe, Constanze and Wolff, Matthias}, title = {How to Do Machine Learning with Small Data? - A Review from an Industrial Perspective}, series = {arXiv}, journal = {arXiv}, abstract = {Artificial intelligence experienced a technological breakthrough in science, industry, and everyday life in the recent few decades. The advancements can be credited to the ever-increasing availability and miniaturization of computational resources that resulted in exponential data growth. However, because of the insufficient amount of data in some cases, employing machine learning in solving complex tasks is not straightforward or even possible. As a result, machine learning with small data experiences rising importance in data science and application in several fields. The authors focus on interpreting the general term of "small data" and their engineering and industrial application role. They give a brief overview of the most important industrial applications of machine learning and small data. Small data is defined in terms of various characteristics compared to big data, and a machine learning formalism was introduced. Five critical challenges of machine learning with small data in industrial applications are presented: unlabeled data, imbalanced data, missing data, insufficient data, and rare events. Based on those definitions, an overview of the considerations in domain representation and data acquisition is given along with a taxonomy of machine learning approaches in the context of small data.}, language = {en} } @misc{KraljevskiDuckhornTschoepeetal., author = {Kraljevski, Ivan and Duckhorn, Frank and Tsch{\"o}pe, Constanze and Schubert, Frank and Wolff, Matthias}, title = {Paper Tissue Softness Rating by Acoustic Emission Analysis}, series = {Applied Sciences}, volume = {13}, journal = {Applied Sciences}, number = {3}, issn = {2076-3417}, doi = {10.3390/app13031670}, abstract = {Softness is one of the essential properties of hygiene tissue products. Reliably measuring it is of utmost importance to ensure the balance between customer expectations and cost-effective tissue production. This study presents a method for assessing softness by analyzing acoustic emissions produced while tearing a tissue specimen. The aim was to train neural network models using the corrected results of human panel tests as the ground truth labels and to predict the tissue softness in two- and three-class recognition tasks. We also investigate the possibility of predicting some production parameters related to the softness property. The results proved that tissue softness and production parameters could be reliably estimated only by the tearing noise.}, language = {en} } @misc{JuKraljevskiNeunuebeletal., author = {Ju, Yong Chul and Kraljevski, Ivan and Neun{\"u}bel, Heiko and Tsch{\"o}pe, Constanze and Wolff, Matthias}, title = {Acoustic Resonance Testing of Small Data on Sintered Cogwheels}, series = {Sensors}, volume = {22}, journal = {Sensors}, number = {15}, issn = {1424-8220}, doi = {10.3390/s22155814}, abstract = {Based on the fact that cogwheels are indispensable parts in manufacturing, we present the acoustic resonance testing (ART) of small data on sintered cogwheels for quality control in the context of non-destructive testing (NDT). Considering the lack of extensive studies on cogwheel data by means of ART in combination with machine learning (ML), we utilize time-frequency domain feature analysis and apply ML algorithms to the obtained feature sets in order to detect damaged samples in two ways: one-class and binary classification. In each case, despite small data, our approach delivers robust performance: All damaged test samples reflecting real-world scenarios are recognized in two one-class classifiers (also called detectors), and one intact test sample is misclassified in binary ones. This shows the usefulness of ML and time-frequency domain feature analysis in ART on a sintered cogwheel dataset.}, language = {en} } @misc{MaierKuhnDuckhornetal., author = {Maier, Isidor Konrad and Kuhn, Johannes and Duckhorn, Frank and Kraljevski, Ivan and Sobe, Daniel and Wolff, Matthias and Tsch{\"o}pe, Constanze}, title = {Word Class Based Language Modeling: A Case of Upper Sorbian, LREC 2022, Marseille}, series = {Language Resources and Evaluation Conference 2022 (LREC 2022), Marseille, 2022-06-13}, journal = {Language Resources and Evaluation Conference 2022 (LREC 2022), Marseille, 2022-06-13}, doi = {10.5281/zenodo.7501145}, pages = {1}, language = {en} }