TY - CHAP A1 - Tschöpe, Constanze A1 - Joneit, Dieter A1 - Duckhorn, Frank A1 - Strecha, Guntram A1 - Hoffmann, Rüdiger A1 - Wolff, Matthias T1 - Voice control for measurement devices T2 - AIA-DAGA 2013, proceedings of the International Conference on Acoustics , 18 - 21 March 2013 in Merano Y1 - 2013 PB - DEGA CY - Berlin ER - TY - CHAP A1 - Hussein, Hussein A1 - Wolff, Matthias A1 - Jokisch, Oliver A1 - Duckhorn, Frank A1 - Strecha, Guntram A1 - Hoffmann, Rüdiger T1 - A hybrid speech signal based algorithm for pitch marking using finite state machines T2 - 9th annual conference of the International Speech Communication Association 2008 (INTERSPEECH 2008), Brisbane, Australia, 22 - 26 September 2008, Vol. 1 Y1 - 2008 SN - 978-1-61567-378-0 SP - 135 EP - 138 PB - Curran CY - Red Hook, NY ER - TY - CHAP A1 - Tschöpe, Constanze A1 - Duckhorn, Frank A1 - Blüthgen, Peter A1 - Richter, Christian A1 - Papsdorf, Gunther A1 - Wolff, Matthias T1 - Miniaturisiertes System zur intelligenten Signalverarbeitung T2 - DGZFP-Jahrestagung 2017, Zerstörungsfreie Materialprüfung, Koblenz, 22.-24.05.2017, Kurzfassungen der Vorträge und Posterbeiträge Y1 - 2017 UR - https://www.google.de/url?sa=t&rct=j&q=&esrc=s&source=web&cd=5&ved=0ahUKEwiOtpCM7-zUAhXBZ1AKHXMyBQYQFghDMAQ&url=https%3A%2F%2Fjahrestagung.dgzfp.de%2FPortals%2F151%2Fdownloads%2FKufa_alles_oP%2520-%2520GESAMT%2520-%2520ohne%2520Anzeigen.pdf%3Fver%3D2017-05-19-135955-633&usg=AFQjCNGFKZug7Xf1FFhVCqafxEFIyDEIeA&cad=rja SP - S. 192 PB - DGZIP CY - Berlin ER - TY - RPRT A1 - Tschöpe, Constanze A1 - Duckhorn, Frank A1 - Wolff, Matthias T1 - Akustische Mustererkennung: Qualitätskontrolle - Vorausschauende Instandhaltung - Zustandsüberwachung Y1 - 2017 UR - https://www.ikts.fraunhofer.de/content/dam/ikts/forschung/energiesysteme_bio_medizintechnik/bio_nanotechnologie/akustische_diagnostik/IKTS_White_Paper_Akustische_Mustererkennung.pdf PB - Fraunhofer-IKTS CY - Dresden ER - TY - GEN A1 - Tschöpe, Constanze A1 - Kraljevski, Ivan A1 - Duckhorn, Frank A1 - Wolff, Matthias T1 - Sprachtechnologie und akustische Mustererkennung in der medizinischen Anwendung T2 - 16. Landeskonferenz „Digitalisierung im Gesundheitswesen“ 2021 Y1 - 2021 UR - https://www.digital-agentur.de/veranstaltungen/telemed UR - https://www.digital-agentur.de/fileadmin/06_Bilddatenbank/Gesundheit/Telemed/Praesentationen/Tscho__pe_Impuls_Telemed21.pdf ER - TY - GEN A1 - Wunderlich, Christian A1 - Tschöpe, Constanze A1 - Duckhorn, Frank T1 - Advanced methods in NDE using machine learning approaches T2 - 44th Annual Review of Progress in Quantitative Nondestructive Evaluation, Provo, Utah, USA, 16-21 July 2017 N2 - 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. Y1 - 2018 U6 - https://doi.org/10.1063/1.5031519 SN - 0094-243X VL - 37 SP - 1 EP - 8 PB - AIP Publishing CY - College Park, Maryland ER - TY - CHAP A1 - Strecha, Guntram A1 - Wolff, Matthias A1 - Duckhorn, Frank A1 - Wittenberg, Sören A1 - Tschöpe, Constanze T1 - The HMM synthesis algorithm of an embedded unified speech recognizer and synthesizer T2 - Proceedings of the Annual Conference of the International Speech Communication Association 2009, Interspeech 2009, 6 - 10 September, 2009, Brighton, UK Y1 - 2009 SP - 1763 EP - 1766 PB - ISCA CY - Brighton ER - TY - GEN A1 - Schmidt, Ralph Rudi A1 - Hildebrand, Jorg A1 - Kraljevski, Ivan A1 - Duckhorn, Frank A1 - Tschöpe, Constanze T1 - A study for laser additive manufacturing quality and material classification using machine learning T2 - 2022 IEEE sensors N2 - 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. KW - Machine learning KW - Additive manufacturing KW - Neural network KW - Quality monitoring KW - Signal processing KW - Artificial intelligence KW - Data analysis Y1 - 2022 SN - 978-1-6654-8464-0 U6 - https://doi.org/10.1109/SENSORS52175.2022.9967311 SN - 2168-9229 SP - 1 EP - 4 PB - Institute of Electrical and Electronics Engineers (IEEE) CY - Piscataway, New Jersey ER - TY - GEN A1 - Opitz, Joerg A1 - Wunderlich, Christian A1 - Bendjus, B. A1 - Cikalova, U. A1 - Wolf, C. A1 - Naumann, S. A1 - Lehmann, A. A1 - Barth, M. A1 - Duckhorn, Frank A1 - Köhler, B. A1 - Tschöke, K. A1 - Windisch, T. A1 - Tschöpe, Constanze A1 - Moritz, T. A1 - Scheithauer, U. T1 - Materialdiagnose und integrale Prüfverfahren für keramische Bauteile T2 - Keramische Zeitschrift N2 - Hochleistungskeramiken findet man heute häufig als kritische Komponente in neuentwickelten Systemen für Zukunftsanwendungen. Die Zuverlässigkeit des gesamten Systems basiert hierbei oft auf der kritischen keramischen Komponente. Für diese oft neuentwickelten keramischen Materialien werden neue Methoden für die Prozesssteuerung, Materialdiagnostik und Strukturüberwachung benötigt. In diesem Artikel werden drei für die Keramikcharakterisierung am Fraunhofer-Institut für 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 Kohärenztomographie und die Klanganalyse in Kombination mit einer entsprechenden akustischen Mustererkennung als leistungsfähige Verfahren für die Materialdiagnostik im Bereich der keramischen Materialien vorgestellt. T2 - Materials diagnostics and integrated testing technology for ceramic parts Y1 - 2016 U6 - https://doi.org/10.1007/BF03400267 SN - 0023-0561 VL - 68 IS - 4-5 SP - 249 EP - 254 PB - Springer Science and Business Media LLC CY - Berlin ; Heidelberg ER - TY - GEN A1 - Kraljevski, Ivan A1 - Duckhorn, Frank A1 - Sobe, Daniel A1 - Tschöpe, Constanze A1 - Wolff, Matthias ED - Grawunder, Sven T1 - Speech-to-text in upper sorbian : current state T2 - Elektronische Sprachsignalverarbeitung 2025 : Tagungsband der 36. Konferenz Halle/Saale, 5.–7. März 2025 N2 - 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. Y1 - 2025 UR - https://www.essv.de/pdf/2025_109_116.pdf SN - 978-3-95908-803-9 SN - 0940-6832 SP - 109 EP - 116 PB - TUDpress CY - Dresden ER -