Graph-PIT: Generalized Permutation Invariant Training for Continuous Separation of Arbitrary Numbers of Speakers
T. von Neumann, K. Kinoshita, C. Boeddeker, M. Delcroix, R. Haeb-Umbach, in: Interspeech 2021, 2021.
LibreCat: /record/26770
DOI: 10.21437/interspeech.2021-1177

Data Science und Big Data in der beruflichen Bildung – Konzeption und Erprobung eines Projektkurses für die Sekundarstufe II
S.A. Opel, M. Schlichtig, in: T. Vollmer, T. Karges, T. Richter, B. Schlömer, S. Schütt-Sayed (Eds.), Sammelband der 27. Fachtagung der BAG Berufliche Bildung, wbv Media GmbH &; Co. KG, Bielefeld, 2020, pp. 176–194.
LibreCat: /record/29298
DOI: https://doi.org/10.3278/6004722w

Anforderungen an Studienordnungen in einer digital vernetzten Welt
C. Caruso, A. Martin, Zeitschrift für Hochschulentwicklung  15 (2020) 195–208.
LibreCat: /record/35300
DOI: 10.3217/zfhe-15-01/10

Auswirkungen der Digitalisierung industrieller Arbeit auf die berufliche und allgemeine Bildung
A. Berisha-Gawlowski, C. Caruso, M. Goller, C. Harteis, in: D. Heisler, J. Meier (Eds.), Digitalisierung am Übergang Schule Beruf Ansätze und Perspektiven in Arbeitsdomänen und beruflicher Förderung, wbv, Bielefeld, 2020, pp. 33–51.
LibreCat: /record/35836
DOI: 10.3278/6004725w

Project cSchule für alled as a mentoring programme in university teacher education: Professionalization processes of student teachers in different practical university phases
B. Kottmann, C. Pieper, Hungarian Educational Research Journal 10 (2020) 232–251.
LibreCat: /record/39817
DOI: 10.1556/063.2020.00022

Gamification in an Augmented Reality Based Virtual Preparation Laboratory Training
M. Alptekin, K. Temmen, in: The Challenges of the Digital Transformation in Education, Springer International Publishing, Cham, 2019.
LibreCat: /record/34123
DOI: 10.1007/978-3-030-11932-4_54

Teaching an Oscilloscope through Progressive Onboarding in an Augmented Reality Based Virtual Laboratory
M. Alptekin, K. Temmen, in: 2019 IEEE Global Engineering Education Conference (EDUCON), 2019, pp. 1047–1054.
LibreCat: /record/34121
DOI: 10.1109/EDUCON.2019.8725038

Literature Reviews in IS Research: What Can Be Learnt from the Past and Other Fields?
G. Schryen, A. Benlian, F. Rowe, G. Shirley, K. Larsen, S. Petter, G. Par\rquote e, G. Wagner, S. Haag, E. Yasasin, Communications of the AIS 40 (2017) 557–569.
LibreCat: /record/5633

Evolvable caches: Optimization of reconfigurable cache mappings for a LEON3/Linux-based multi-core processor
N. Ho, P. Kaufmann, M. Platzner, in: 2017 International Conference on Field Programmable Technology (ICFPT), 2017, pp. 215–218.
LibreCat: /record/10676
DOI: 10.1109/FPT.2017.8280144

Unsupervised adaptation of a denoising autoencoder by Bayesian Feature Enhancement for reverberant asr under mismatch conditions
J. Heymann, R. Haeb-Umbach, P. Golik, R. Schlueter, in: Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference On, 2015, pp. 5053–5057.
LibreCat: /record/11813
DOI: 10.1109/ICASSP.2015.7178933

An Overview of Noise-Robust Automatic Speech Recognition
J. Li, L. Deng, Y. Gong, R. Haeb-Umbach, IEEE Transactions on Audio, Speech and Language Processing 22 (2014) 745–777.
LibreCat: /record/11867
DOI: 10.1109/TASLP.2014.2304637

Parameter estimation and classification of censored Gaussian data with application to WiFi indoor positioning
M.K. Hoang, R. Haeb-Umbach, in: 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013), 2013, pp. 3721–3725.
LibreCat: /record/11816
DOI: 10.1109/ICASSP.2013.6638353

Parameter Estimation of a State-Space Model of Noise for Robust Speech Recognition
S. Windmann, R. Haeb-Umbach, IEEE Transactions on Audio, Speech, and Language Processing 17 (2009) 1577–1590.
LibreCat: /record/11938
DOI: 10.1109/TASL.2009.2023172

Iterative Speech Enhancement using a Non-Linear Dynamic State Model of Speech and its Parameters
S. Windmann, R. Haeb-Umbach, in: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006), 2006, p. I.
LibreCat: /record/11943
DOI: 10.1109/ICASSP.2006.1660058

Automatic generation of phonetic regression class trees for MLLR adaptation
R. Haeb-Umbach, IEEE Transactions on Speech and Audio Processing 9 (2001) 299–302.
LibreCat: /record/11778
DOI: 10.1109/89.906003