TY - GEN A1 - Beim Graben, Peter A1 - Blutner, Reinhard T1 - Quantum approaches to music cognition T2 - Journal of Mathematical Psychology N2 - Quantum cognition emerged as an important discipline of mathematical psychology during the last two decades. Using abstract analogies between mental phenomena and the formal framework of physical quantum theory, quantum cognition demonstrated its ability to resolve several puzzles from cognitive psychology. Until now, quantum cognition essentially exploited ideas from projective (Hilbert space) geometry, such as quantum probability or quantum similarity. However, many powerful tools provided by physical quantum theory, e.g., symmetry groups have not been utilized in the field of quantum cognition research sofar. Inspired by seminal work by Guerino Mazzola on the symmetries of tonal music, our study aims at elucidating and reconciling static and dynamic tonal attraction phenomena in music psychology within the quantum cognition framework. Based on the fundamental principles of octave equivalence, fifth similarity and transposition symmetry of tonal music that are reflected by the structure of the circle of fifths, we develop different wave function descriptions over this underlying tonal space. We present quantum models for static and dynamic tonal attraction and compare them with traditional computational models in musicology. Our approach replicates and also improves predictions based on symbolic models of music perception. KW - Music psychology KW - Tonal attraction KW - Quantum cognition KW - Tonal space Y1 - 2019 U6 - https://doi.org/10.1016/j.jmp.2019.03.002 SN - 0022-2496 VL - 91 SP - 38 EP - 50 ER - TY - GEN A1 - Beim Graben, Peter A1 - Römer, Ronald A1 - Meyer, Werner A1 - Huber, Markus A1 - Wolff, Matthias T1 - Reinforcement learning of minimalist numeral grammars T2 - 10th IEEE International Conference on Cognitive Infocommunications (CogInfoCom), Naples, Italy N2 - Speech-controlled user interfaces facilitate the operation of devices and household functions to laymen. State-of-the-art language technology scans the acoustically analyzed speech signal for relevant keywords that are subsequently inserted into semantic slots to interpret the user's intent. In order to develop proper cognitive information and communication technologies, simple slot-filling should be replaced by utterance meaning transducers (UMT) that are based on semantic parsers and a mental lexicon, comprising syntactic, phonetic and semantic features of the language under consideration. This lexicon must be acquired by a cognitive agent during interaction with its users. We outline a reinforcement learning algorithm for the acquisition of the syntactic morphology and arithmetic semantics of English numerals, based on minimalist grammar (MG), a recent computational implementation of generative linguistics. Number words are presented to the agent by a teacher in form of utterance meaning pairs (UMP) where the meanings are encoded as arithmetic terms. Since MG encodes universal linguistic competence through inference rules, thereby separating innate linguistic knowledge from the contingently acquired lexicon, our approach unifies generative grammar and reinforcement learning, hence potentially resolving the still pending Chomsky-Skinner controversy. Y1 - 2019 UR - https://arxiv.org/abs/1906.04447 SN - 978-1-7281-4793-2 SN - 978-1-7281-4792-5 SN - 978-1-7281-4794-9 U6 - https://doi.org/10.1109/CogInfoCom47531.2019.9089924 SN - 2380-7350 ER - TY - GEN A1 - Werner, Steffen A1 - Eichner, Matthias A1 - Wolff, Matthias A1 - Hoffmann, Rüdiger T1 - Towards spontaneous speech synthesis - Utilizing language model information in TTS T2 - IEEE transactions on speech and audio processing Y1 - 2004 U6 - https://doi.org/10.1109/TSA.2004.828635 SN - 1063-6676 VL - 12 IS - 4 SP - 436 EP - 445 ER - TY - JOUR A1 - Fellbaum, Klaus T1 - Electronic speech processing for persons with disabilities Y1 - 2008 ER - TY - GEN A1 - Duckhorn, Frank A1 - Huber, Markus A1 - Meyer, Werner A1 - Jokisch, Oliver A1 - Tschöpe, Constanze A1 - Wolff, Matthias ED - Lacerda, Francisco T1 - Towards an Autarkic Embedded Cognitive User Interface T2 - Proceedings Interspeech 2017, 20-24 August 2017, Stockholm N2 - ucuikt2015 Y1 - 2017 UR - http://www.isca-speech.org/archive/Interspeech_2017/ U6 - https://doi.org/10.21437/Interspeech.2017 SP - 3435 EP - 3436 PB - ISCA ER - TY - GEN A1 - Duckhorn, Frank A1 - Wolff, Matthias A1 - Hoffmann, Rüdiger T1 - A new Epsilon Filter for Efficient Composition of Weighted Finite-State Transducers T2 - Interspeech 2011, 12th annual conference of the International Speech Communication Association 2011, Florence, Italy, 27 - 31 August 2011 Y1 - 2011 UR - https://www.isca-speech.org/archive/archive_papers/interspeech_2011/i11_0897.pdf SP - 897 EP - 900 PB - ISCA ER - TY - THES A1 - Wersenyi, György T1 - HRTFs in human localization: Measurement, spectral evaluation and practical use in virtual audio environment KW - HRTF KW - Localisation KW - GUIB KW - Acoustic KW - Measurement KW - Binaural KW - Dummyhead KW - Hearing KW - Virtual KW - Headphone KW - Psychoacoustic KW - Information Y1 - 2002 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:co1-000000234 PB - Brandenburgische Techn. Univ. CY - Cottbus ER - TY - THES A1 - Marí Hilario, Joan T1 - Discriminative Connectionist Approaches for Automatic Speech Recognition in Cars KW - Speech recognition KW - Noise robustness KW - Evalution KW - AURORA KW - Connectionist KW - Neural net KW - Non-linear feature reduction KW - Multi-Stream Y1 - 2004 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:co1-000000649 ER - TY - GEN A1 - Tschöpe, Constanze A1 - Mühle, Maximilian A1 - Ju, Yong Chul A1 - Kraljevski, Ivan A1 - Wolff, Matthias T1 - Künstliche Intelligenz in der ZfP - Welchen Beitrag kann KI in der ZfP leisten? T2 - DGZfP-Jahrestagung 2021, 10.-11. Mai N2 - Künstliche Intelligenz zieht derzeit in alle Bereiche der Gesellschaft und des Lebens ein. Aber welchen Stellenwert hat sie momentan auf dem Gebiet der zerstörungsfreien Prüfung? Was kann KI leisten? Welche Herausforderungen müssen erfolgreich bewältigt werden? Gibt es das eine KI-Verfahren, welches prinzipiell für ZfP geeignet ist? Bei der Bauteil- und Materialprüfung während und unmittelbar nach der Herstellung, der Überwachung von Verschleißteilen in Maschinen und Anlagen oder der Schadensdetektion an Bauteilen und Komponenten liefern ZfP-Verfahren Daten, die bewertet werden müssen. Obwohl inzwischen sehr leistungsfähige Toolkits verfügbar sind, erfordert der optimale Einsatz der KI für ein ZfP-Verfahren oftmals mehr. Die meisten Kunden möchten nicht nur eine Lösung 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. Abhängig 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 für die KI-Verfahren zur Klassifikation bilden. Der Beitrag liefert einen Überblick über KI-Verfahren und deren Anwendungen in der zerstörungsfreien Prüfung. Zahlreiche Beispiele und Ergebnisse werden vorgestellt, um die Mannigfaltigkeit des Einsatzes in der ZfP und der bestehenden Möglichkeiten zu demonstrieren. Y1 - 2017 UR - https://jt2021.dgzfp.de/portals/jt2021/bb176/inhalt/autoren.htm#T SN - 978-3-947971-18-3 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 - Kraljevski, Ivan A1 - Duckhorn, Frank A1 - Tschöpe, Constanze A1 - Schubert, Frank A1 - Wolff, Matthias T1 - Paper Tissue Softness Rating by Acoustic Emission Analysis T2 - Applied Sciences N2 - 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. KW - acoustic emission KW - machine learning KW - tissue softness analysis Y1 - 2022 U6 - https://doi.org/10.3390/app13031670 SN - 2076-3417 VL - 13 IS - 3 ER -