@inbook{34123,
  abstract     = {{Through technological progress during recent years, Augmented Reality (AR) technology can be used on ordinary smartphones with applications (Apps) in many formal and informal learning environments and educational institutions (e.g. [1, 2]). It is emerging as a suitable technology for teaching psychomotor skills. Simultaneously, gamification has become increasingly popular in the teaching field, providing famous examples, such as Duolingo (for the acquisition of foreign languages) or Codecademy (for learning programming languages) [3]. Many papers have already highlighted the beneficial aspects of gamification and AR for education and teaching (e.g. [1, 2, 4, 5]. While gamification is useful for improving students’ motivation and engagement, AR can be applied to teach them operational skills without any time, costs and place constraints. Hence, this opens up numerous possibilities and forms to combine these two aspects (AR and gamification) for higher education teaching. However, there has been less research focusing on how gamification and AR can be combined in a useful manner to keep up students’ initial motivation aroused through novelty effects of AR learning environments. Accordingly, this paper will present such a gamification concept for an AR based virtual preparation laboratory training to overcome the risk of demotivation, once AR will settle as a mainstream technology such as learning videos. The focus of the AR-App – presently being developed at the University of Paderborn – is to remedy the students’ lack of practical skills when operating electro-technical laboratory equipment during their compulsory laboratory training.}},
  author       = {{Alptekin, Mesut and Temmen, Katrin}},
  booktitle    = {{The Challenges of the Digital Transformation in Education}},
  isbn         = {{9783030119317}},
  issn         = {{2194-5357}},
  keywords     = {{Augmented Reality, Laboratory Training, Engineering Education, Gamification}},
  location     = {{Kos Island, Greece}},
  publisher    = {{Springer International Publishing}},
  title        = {{{Gamification in an Augmented Reality Based Virtual Preparation Laboratory Training}}},
  doi          = {{10.1007/978-3-030-11932-4_54}},
  year         = {{2019}},
}

@inbook{57889,
  abstract     = {{During the past decade, there has been an increase of pedagogical research under conditions of posthuman theories, such as the Actor Network Theory or post-phenomenology. Yet, there has not been much research on the materiality of music pedagogical practices. This article introduces an ongoing grounded-theory study on the role of things (e.g., music instruments, black board, or digital devices) within the music classroom. Results from the analysis of group discussions and interviews with student teachers show tensions between personal preferences, school conventions, and material conventions within the process of introducing things into the classroom. (DIPF/Orig.)}},
  author       = {{Godau, Marc}},
  booktitle    = {{Soziale Aspekte des Musiklernens}},
  editor       = {{Clausen, Bernd and Dreßler, Susanne}},
  keywords     = {{Interview, Lehrer, Musical education, Musikpädagogik, Musikunterricht, Teacher, Music lessons, Qualitative Forschung, Qualitative research, Teaching of music, Object, Objekt, Ding, Handlung, Practice, Praxis, Probationary teacher training, Referendariat}},
  pages        = {{43–55}},
  publisher    = {{Waxmann}},
  title        = {{{Wie kommen die Dinge in den Musikunterricht? Zur Materialität musikpädagogischer Praxis am Beispiel divergierender Orientierungen im Kontext unterrichtsbezogenen Handelns angehender Lehrkräfte}}},
  year         = {{2018}},
}

@article{5633,
  abstract     = {{Literature reviews (LRs) are recognized for their increasing impact in the information systems literature. Methodologists have drawn attention to the question of how we can leverage the value of LRs to preserve and generate knowledge. The panelists who participated in the discussion of ?Standalone Literature Reviews in IS Research: What Can Be Learnt from the Past and Other Fields?? at ICIS 2016 in Dublin acknowledged this significant issue and debated a) what the IS field can learn from other fields and where IS-specific challenges occur, b) how the IS field should move forward to foster the genre of LRs, and c) what best practices are to train doctoral IS students in publishing LRs. This article reports the key takeaways of this panel discussion. Guidance for IS scholars is provided on how to conduct LRs that contribute to the cumulative knowledge development within and across the IS field to best prepare the next generation of IS scholars.}},
  author       = {{Schryen, Guido and Benlian, Alexander and Rowe, Frantz and Shirley, Gregor and Larsen, Kai and Petter, Stacie and Par{\'e}, Guy and Wagner, Gerit and Haag, Steffi and Yasasin, Emrah}},
  issn         = {{1529-3181}},
  journal      = {{Communications of the AIS}},
  keywords     = {{Literature Review, Review Methodology, Research Methodology, Doctoral Training}},
  pages        = {{557 -- 569}},
  publisher    = {{Association for Information Systems (AIS)}},
  title        = {{{Literature Reviews in IS Research: What Can Be Learnt from the Past and Other Fields?}}},
  volume       = {{40}},
  year         = {{2017}},
}

@inproceedings{10676,
  author       = {{Ho, Nam and Kaufmann, Paul and Platzner, Marco}},
  booktitle    = {{2017 International Conference on Field Programmable Technology (ICFPT)}},
  keywords     = {{Linux, cache storage, microprocessor chips, multiprocessing systems, LEON3-Linux based multicore processor, MiBench suite, block sizes, cache adaptation, evolvable caches, memory-to-cache-index mapping function, processor caches, reconfigurable cache mapping optimization, reconfigurable hardware technology, replacement strategies, standard Linux OS, time a complete hardware implementation, Hardware, Indexes, Linux, Measurement, Multicore processing, Optimization, Training}},
  pages        = {{215--218}},
  title        = {{{Evolvable caches: Optimization of reconfigurable cache mappings for a LEON3/Linux-based multi-core processor}}},
  doi          = {{10.1109/FPT.2017.8280144}},
  year         = {{2017}},
}

@inproceedings{11813,
  abstract     = {{The parametric Bayesian Feature Enhancement (BFE) and a datadriven Denoising Autoencoder (DA) both bring performance gains in severe single-channel speech recognition conditions. The first can be adjusted to different conditions by an appropriate parameter setting, while the latter needs to be trained on conditions similar to the ones expected at decoding time, making it vulnerable to a mismatch between training and test conditions. We use a DNN backend and study reverberant ASR under three types of mismatch conditions: different room reverberation times, different speaker to microphone distances and the difference between artificially reverberated data and the recordings in a reverberant environment. We show that for these mismatch conditions BFE can provide the targets for a DA. This unsupervised adaptation provides a performance gain over the direct use of BFE and even enables to compensate for the mismatch of real and simulated reverberant data.}},
  author       = {{Heymann, Jahn and Haeb-Umbach, Reinhold and Golik, P. and Schlueter, R.}},
  booktitle    = {{Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on}},
  keywords     = {{codecs, signal denoising, speech recognition, Bayesian feature enhancement, denoising autoencoder, reverberant ASR, single-channel speech recognition, speaker to microphone distances, unsupervised adaptation, Adaptation models, Noise reduction, Reverberation, Speech, Speech recognition, Training, deep neuronal networks, denoising autoencoder, feature enhancement, robust speech recognition}},
  pages        = {{5053--5057}},
  title        = {{{Unsupervised adaptation of a denoising autoencoder by Bayesian Feature Enhancement for reverberant asr under mismatch conditions}}},
  doi          = {{10.1109/ICASSP.2015.7178933}},
  year         = {{2015}},
}

@article{11867,
  abstract     = {{New waves of consumer-centric applications, such as voice search and voice interaction with mobile devices and home entertainment systems, increasingly require automatic speech recognition (ASR) to be robust to the full range of real-world noise and other acoustic distorting conditions. Despite its practical importance, however, the inherent links between and distinctions among the myriad of methods for noise-robust ASR have yet to be carefully studied in order to advance the field further. To this end, it is critical to establish a solid, consistent, and common mathematical foundation for noise-robust ASR, which is lacking at present. This article is intended to fill this gap and to provide a thorough overview of modern noise-robust techniques for ASR developed over the past 30 years. We emphasize methods that are proven to be successful and that are likely to sustain or expand their future applicability. We distill key insights from our comprehensive overview in this field and take a fresh look at a few old problems, which nevertheless are still highly relevant today. Specifically, we have analyzed and categorized a wide range of noise-robust techniques using five different criteria: 1) feature-domain vs. model-domain processing, 2) the use of prior knowledge about the acoustic environment distortion, 3) the use of explicit environment-distortion models, 4) deterministic vs. uncertainty processing, and 5) the use of acoustic models trained jointly with the same feature enhancement or model adaptation process used in the testing stage. With this taxonomy-oriented review, we equip the reader with the insight to choose among techniques and with the awareness of the performance-complexity tradeoffs. The pros and cons of using different noise-robust ASR techniques in practical application scenarios are provided as a guide to interested practitioners. The current challenges and future research directions in this field is also carefully analyzed.}},
  author       = {{Li, Jinyu and Deng, Li and Gong, Yifan and Haeb-Umbach, Reinhold}},
  journal      = {{IEEE Transactions on Audio, Speech and Language Processing}},
  keywords     = {{Speech recognition, compensation, distortion modeling, joint model training, noise, robustness, uncertainty processing}},
  number       = {{4}},
  pages        = {{745--777}},
  title        = {{{An Overview of Noise-Robust Automatic Speech Recognition}}},
  doi          = {{10.1109/TASLP.2014.2304637}},
  volume       = {{22}},
  year         = {{2014}},
}

@inproceedings{11816,
  abstract     = {{In this paper, we consider the Maximum Likelihood (ML) estimation of the parameters of a GAUSSIAN in the presence of censored, i.e., clipped data. We show that the resulting Expectation Maximization (EM) algorithm delivers virtually biasfree and efficient estimates, and we discuss its convergence properties. We also discuss optimal classification in the presence of censored data. Censored data are frequently encountered in wireless LAN positioning systems based on the fingerprinting method employing signal strength measurements, due to the limited sensitivity of the portable devices. Experiments both on simulated and real-world data demonstrate the effectiveness of the proposed algorithms.}},
  author       = {{Hoang, Manh Kha and Haeb-Umbach, Reinhold}},
  booktitle    = {{38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)}},
  issn         = {{1520-6149}},
  keywords     = {{Gaussian processes, Global Positioning System, convergence, expectation-maximisation algorithm, fingerprint identification, indoor radio, signal classification, wireless LAN, EM algorithm, ML estimation, WiFi indoor positioning, censored Gaussian data classification, clipped data, convergence properties, expectation maximization algorithm, fingerprinting method, maximum likelihood estimation, optimal classification, parameters estimation, portable devices sensitivity, signal strength measurements, wireless LAN positioning systems, Convergence, IEEE 802.11 Standards, Maximum likelihood estimation, Parameter estimation, Position measurement, Training, Indoor positioning, censored data, expectation maximization, signal strength, wireless LAN}},
  pages        = {{3721--3725}},
  title        = {{{Parameter estimation and classification of censored Gaussian data with application to WiFi indoor positioning}}},
  doi          = {{10.1109/ICASSP.2013.6638353}},
  year         = {{2013}},
}

@article{11938,
  abstract     = {{In this paper, parameter estimation of a state-space model of noise or noisy speech cepstra is investigated. A blockwise EM algorithm is derived for the estimation of the state and observation noise covariance from noise-only input data. It is supposed to be used during the offline training mode of a speech recognizer. Further a sequential online EM algorithm is developed to adapt the observation noise covariance on noisy speech cepstra at its input. The estimated parameters are then used in model-based speech feature enhancement for noise-robust automatic speech recognition. Experiments on the AURORA4 database lead to improved recognition results with a linear state model compared to the assumption of stationary noise.}},
  author       = {{Windmann, Stefan and Haeb-Umbach, Reinhold}},
  journal      = {{IEEE Transactions on Audio, Speech, and Language Processing}},
  keywords     = {{AURORA4 database, blockwise EM algorithm, covariance analysis, linear state model, noise covariance, noise-robust automatic speech recognition, noisy speech cepstra, offline training mode, parameter estimation, speech recognition, speech recognition equipment, speech recognizer, state-space methods, state-space model}},
  number       = {{8}},
  pages        = {{1577--1590}},
  title        = {{{Parameter Estimation of a State-Space Model of Noise for Robust Speech Recognition}}},
  doi          = {{10.1109/TASL.2009.2023172}},
  volume       = {{17}},
  year         = {{2009}},
}

@inproceedings{11943,
  abstract     = {{A marginalized particle filter is proposed for performing single channel speech enhancement with a non-linear dynamic state model. The system consists of a particle filter for tracking line spectral pair (LSP) parameters and a Kalman filter per particle for speech enhancement. The state model for the LSPs has been learnt on clean speech training data. In our approach parameters and speech samples are processed at different time scales by assuming the parameters to be constant for small blocks of data. Further enhancement is obtained by an iteration which can be applied on these small blocks. The experiments show that similar SNR gains are obtained as with the Kalman-LM-iterative algorithm. However better values of the noise level and the log-spectral distance are achieved}},
  author       = {{Windmann, Stefan and Haeb-Umbach, Reinhold}},
  booktitle    = {{IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006)}},
  keywords     = {{clean speech training data, iterative methods, iterative speech enhancement, Kalman filter, Kalman filters, Kalman-LM-iterative algorithm, line spectral pair parameters, log-spectral distance, marginalized particle filter, noise level, nonlinear dynamic state speech model, particle filtering (numerical methods), single channel speech enhancement, SNR gains, speech enhancement, speech samples}},
  pages        = {{I}},
  title        = {{{Iterative Speech Enhancement using a Non-Linear Dynamic State Model of Speech and its Parameters}}},
  doi          = {{10.1109/ICASSP.2006.1660058}},
  volume       = {{1}},
  year         = {{2006}},
}

@article{11778,
  abstract     = {{In this paper, it is shown that a correlation criterion is the appropriate criterion for bottom-up clustering to obtain broad phonetic class regression trees for maximum likelihood linear regression (MLLR)-based speaker adaptation. The correlation structure among speech units is estimated on the speaker-independent training data. In adaptation experiments the tree outperformed a regression tree obtained from clustering according to closeness in acoustic space and achieved results comparable with those of a manually designed broad phonetic class tree}},
  author       = {{Haeb-Umbach, Reinhold}},
  journal      = {{IEEE Transactions on Speech and Audio Processing}},
  keywords     = {{acoustic space, adaptation experiments, automatic generation, bottom-up clustering, broad phonetic class regression trees, correlation criterion, correlation methods, maximum likelihood estimation, maximum likelihood linear regression based speaker adaptation, MLLR adaptation, pattern clustering, phonetic regression class trees, speaker-independent training data, speech recognition, speech units, statistical analysis, trees (mathematics)}},
  number       = {{3}},
  pages        = {{299--302}},
  title        = {{{Automatic generation of phonetic regression class trees for MLLR adaptation}}},
  doi          = {{10.1109/89.906003}},
  volume       = {{9}},
  year         = {{2001}},
}