Professur für Informatik (Mustererkennung)

Adresse:
Martensstraße 3
91058 Erlangen


Forschungsprojekt(e)


(Training Network on Automatic Processing of PAthological Speech):
Modelling the progression of neurological diseases
Prof. Dr.-Ing. Elmar Nöth; Juan Vasquez Correa
(01.05.2018)


AUWL: Automatic Learner-Feedback-System
Prof. Dr.-Ing. Elmar Nöth
(01.07.2010 - 31.12.2011)


Automatische Analyse von Lautbildungsstörungen bei Kindern und Jugendlichen mit Lippen-Kiefer-Gaumenspalten (LKG)
Ulrich Eysholdt; Prof. Dr.-Ing. Andreas Maier; Prof. Dr.-Ing. Elmar Nöth
(01.04.2010 - 31.03.2013)



Publikationen (Download BibTeX)

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Bergler, C., Schröter, H., Cheng, R.X., Barth, V., Weber, M., Nöth, E.,... Maier, A. (2019). ORCA-SPOT: An Automatic Killer Whale Sound Detection Toolkit Using Deep Learning. Scientific Reports, 9(1). https://dx.doi.org/10.1038/s41598-019-47335-w
Baier, L., Frommherz, J., Nöth, E., Donhauser, T., Schuderer, P., & Franke, J. (2019). Identifying failure root causes by visualizing parameter interdependencies with spectrograms. Journal of Manufacturing Systems, 53, 11-17. https://dx.doi.org/10.1016/j.jmsy.2019.08.002
Rios-Urrego, C.D., Vargas-Bonilla, F., Nöth, E., Lopera, F., & Orozco Arroyave, J.R. (2019). Analysis and evaluation of handwriting in patients with Parkinson’s disease using kinematic, geometrical, and non-linear features. Computer Methods and Programs in Biomedicine, 173, 43-52. https://dx.doi.org/10.1016/j.cmpb.2019.03.005
Schröter, H., Nöth, E., Maier, A., Cheng, R., Barth, V., & Bergler, C. (2019). Segmentation, Classification, and Visualization of Orca Calls Using Deep Learning. In ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 8231-8235). Brighton, GB: IEEE.
Fritsch, J., Wankerl, S., & Nöth, E. (2019). Automatic Diagnosis of Alzheimer's Disease Using Neural Network Language Models. In ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings (pp. 5841-5845). Brighton, GBR: Institute of Electrical and Electronics Engineers Inc..
Schwinn, L., Haderlein, T., Nöth, E., & Maier, A. (2019). Impact of Pathologies on Automatic Age Estimation. In Deutsche Gesellschaft für Akustik e.V. (Eds.), Fortschritte der Akustik - DAGA 2019 (pp. 939-942). Rostock, DE: Rostock: Deutsche Gesellschaft für Akustik e.V. (DEGA).
Vasquez Correa, J., Arias Vergara, T., Rafael Orozco-Arroyave, J., Eskofier, B., Klucken, J., & Nöth, E. (2018). Multimodal assessment of Parkinson's disease: a deep learning approach. IEEE Journal of Biomedical and Health Informatics. https://dx.doi.org/10.1109/JBHI.2018.2866873
Rafael Orozco-Arroyave, J., Vasquez Correa, J., Francisco Vargas-Bonilla, J., Arora, R., Dehak, N., Nidadavolu, P.S.,... Nöth, E. (2018). NeuroSpeech: An open-source software for Parkinson's speech analysis. Digital Signal Processing, 77(0), 207-221. https://dx.doi.org/10.1016/j.dsp.2017.07.004
Ferrer Riesgo, C.A., Haderlein, T., Maryn, Y., de Bodt, M., & Nöth, E. (2018). Collinearity and Sample Coverage Issues in the Objective Measurement of Vocal Quality: The Case of Roughness and Breathiness. Journal of Speech Language and Hearing Research, 61(1), 1-24. https://dx.doi.org/10.1044/2017_JSLHR-S-17-0136
Vasquez Correa, J., Orozco-Arroyave, J.R., Bocklet, T., & Nöth, E. (2018). Towards an automatic evaluation of the dysarthria level of patients with Parkinson's disease. Journal of Communication Disorders, 76(0), 21-36. https://dx.doi.org/10.1016/j.jcomdis.2018.08.002
Arias Vergara, T., Vasquez Correa, J., Rafael Orozco-Arroyave, J., & Nöth, E. (2018). Speaker models for monitoring Parkinson’s disease progression considering different communication channels and acoustic conditions. Speech Communication, 101, 11-25. https://dx.doi.org/10.1016/j.specom.2018.05.007
Garcia, N., Vasquez Correa, J., Arroyave, J.R.O., & Nöth, E. (2018). Multimodal i-vectors to Detect and Evaluate Parkinson's Disease. In Proceedings of INTERSPEECH (pp. 2349-2353).
Vasquez Correa, J., Arias Vergara, T., Orozco-Arroyave, J.R., & Nöth, E. (2018). A Multitask Learning Approach to Assess the Dysarthria Severity in Patients with Parkinson's Disease. In Proceedings of INTERSPEECH (pp. 456-460).
Felipe Parra-Gallego, L., Arias-Vergara, T., Camilo Vasquez-Correa, J., Garcia-Ospina, N., Rafael Orozco-Arroyave, J., & Nöth, E. (2018). Automatic Intelligibility Assessment of Parkinson's Disease with Diadochokinetic Exercises. In APPLIED COMPUTER SCIENCES IN ENGINEERING, WEA 2018, PT II (pp. 223-230). Medellin, CO: BERLIN: SPRINGER-VERLAG BERLIN.
Arias Vergara, T., Vasquez Correa, J., Rafael Orozco-Arroyave, J., Klumpp, P., & Nöth, E. (2018). Unontrusive Monitoring of Speech Impairments of Parinson's Disease Patients Through Mobile Devices. In Proceedings of the 43rd IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 6004-6008).
Vasquez Correa, J., Garcia, N., Orozco-Arroyave, J.R., Cernak, M., & Nöth, E. (2018). Phonological posteriors and GRU recurrent units to assess speech impairments of patients with Parkinson’s disease. In Prof. Dr. Petr Sojka, Aleš Horák, Ivan Kopeček, Karel Pala (Eds.), Text, Speech, and Dialogue. (pp. 453-461). Springer Interational.
Arias Vergara, T., Vasquez Correa, J., Orozco Arroyave, J.R., Klumpp, P., & Nöth, E. (2018). Unobtrusive Monitoring of Speech Impairments of Parkinson's Disease Patients Through Mobile Devices. In Proceedings of the 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 (pp. 6004-6008). Institute of Electrical and Electronics Engineers Inc..
Klumpp, P., Fritsch, J.D., & Nöth, E. (2018). ANN-based Alzheimer’s disease classification from bag of words. In Speech Communication - 13. ITG-Fachtagung Sprachkommunikation.
Andrea Perez-Toro, P., Camilo Vasquez-Correa, J., Arias-Vergara, T., Garcia-Ospina, N., Rafael Orozco-Arroyave, J., & Nöth, E. (2018). A Non-linear Dynamics Approach to Classify Gait Signals of Patients with Parkinson's Disease. In APPLIED COMPUTER SCIENCES IN ENGINEERING, WEA 2018, PT II (pp. 268-278). Medellin, COLOMBIA: BERLIN: SPRINGER-VERLAG BERLIN.
Martindale, C., Hönig, F.T., Strohrmann, C., & Eskofier, B. (2017). Smart Annotation of Cyclic Data Using Hierarchical Hidden Markov Models. Sensors, 17(10). https://dx.doi.org/10.3390/s17102328

Zuletzt aktualisiert 2019-24-04 um 10:31