@article{3963, abstract = {{Whispering gallery modes (WGMs) were observed in 60 nm thin cubic AlN microdisk resonators containing a single layer of non-polar cubic GaN quantum dots. Freestanding microdisks were patterned by means of electron beam lithography and a two step reactive ion etching process. Micro-photoluminescence spectroscopy investigations were performed for optical characterization. We analyzed the mode spacing for disk diameters ranging from 2-4 lm. Numerical investigations using three dimensional finite difference time domain calculations were in good agreement with the experimental data. Whispering gallery modes of the radial orders 1 and 2 were identified by means of simulated mode field distributions.}}, author = {{Bürger, M. and Ruth, M. and Declair, S. and Förstner, Jens and Meier, Cedrik and As, Donat Josef}}, issn = {{0003-6951}}, journal = {{Applied Physics Letters}}, keywords = {{tet_topic_qd, tet_topic_microdisk}}, number = {{8}}, pages = {{081105}}, publisher = {{AIP Publishing}}, title = {{{Whispering gallery modes in zinc-blende AlN microdisks containing non-polar GaN quantum dots}}}, doi = {{10.1063/1.4793653}}, volume = {{102}}, year = {{2013}}, } @unpublished{538, abstract = {{We present a new technique to realize attribute-based encryption (ABE) schemes secure in the standard model against chosen-ciphertext attacks (CCA-secure). Our approach is to extend certain concrete chosen-plaintext secure (CPA-secure) ABE schemes to achieve more efficient constructions than the known generic constructions of CCA-secure ABE schemes. We restrict ourselves to the construction of attribute-based key encapsulation mechanisms (KEMs) and present two concrete CCA-secure schemes: a key-policy attribute-based KEM that is based on Goyal's key-policy ABE and a ciphertext-policy attribute-based KEM that is based on Waters' ciphertext-policy ABE. To achieve our goals, we use an appropriate hash function and need to extend the public parameters and the ciphertexts of the underlying CPA-secure encryption schemes only by a single group element. Moreover, we use the same hardness assumptions as the underlying CPA-secure encryption schemes.}}, author = {{Blömer, Johannes and Liske, Gennadij}}, publisher = {{Universität Paderborn}}, title = {{{Direct Chosen-Ciphertext Secure Attribute-Based Key Encapsulations without Random Oracles}}}, year = {{2013}}, } @article{5575, author = {{Wex, Felix and Schryen, Guido and Neumann, Dirk}}, journal = {{International Journal of Information Systems for Crisis Response and Management}}, number = {{4}}, pages = {{63--80}}, publisher = {{IGI Publ.}}, title = {{{Assignments of Collaborative Rescue Units during Emergency Response}}}, volume = {{5}}, year = {{2013}}, } @inproceedings{5576, author = {{Wex, Felix and Schryen, Guido and Neumann, Dirk}}, booktitle = {{Proceedings of the 46th Hawaii International Conference on System Science}}, title = {{{Decision Modeling for Assignments of Collaborative Rescue Units during Emergency Response}}}, year = {{2013}}, } @article{5637, author = {{Schryen, Guido}}, journal = {{European Journal of Information Systems (EJIS)}}, number = {{2}}, pages = {{139--169}}, title = {{{Revisiting IS Business Value Research: What we already know, what we still need to know, and how we can get there}}}, volume = {{22}}, year = {{2013}}, } @inproceedings{11740, abstract = {{In this contribution we derive the Maximum A-Posteriori (MAP) estimates of the parameters of a Gaussian Mixture Model (GMM) in the presence of noisy observations. We assume the distortion to be white Gaussian noise of known mean and variance. An approximate conjugate prior of the GMM parameters is derived allowing for a computationally efficient implementation in a sequential estimation framework. Simulations on artificially generated data demonstrate the superiority of the proposed method compared to the Maximum Likelihood technique and to the ordinary MAP approach, whose estimates are corrected by the known statistics of the distortion in a straightforward manner.}}, author = {{Chinaev, Aleksej and Haeb-Umbach, Reinhold}}, booktitle = {{38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)}}, issn = {{1520-6149}}, keywords = {{Gaussian noise, maximum likelihood estimation, parameter estimation, GMM parameter, Gaussian mixture model, MAP estimation, Map-based estimation, maximum a-posteriori estimation, maximum likelihood technique, noisy observation, sequential estimation framework, white Gaussian noise, Additive noise, Gaussian mixture model, Maximum likelihood estimation, Noise measurement, Gaussian mixture model, Maximum a posteriori estimation, Maximum likelihood estimation}}, pages = {{3352--3356}}, title = {{{MAP-based Estimation of the Parameters of a Gaussian Mixture Model in the Presence of Noisy Observations}}}, doi = {{10.1109/ICASSP.2013.6638279}}, year = {{2013}}, } @inproceedings{11742, abstract = {{In this paper we present an improved version of the recently proposed Maximum A-Posteriori (MAP) based noise power spectral density estimator. An empirical bias compensation and bandwidth adjustment reduce bias and variance of the noise variance estimates. The main advantage of the MAP-based postprocessor is its low estimation variance. The estimator is employed in the second stage of a two-stage single-channel speech enhancement system, where eight different state-of-the-art noise tracking algorithms were tested in the first stage. While the postprocessor hardly affects the results in stationary noise scenarios, it becomes the more effective the more nonstationary the noise is. The proposed postprocessor was able to improve all systems in babble noise w.r.t. the perceptual evaluation of speech quality performance.}}, author = {{Chinaev, Aleksej and Haeb-Umbach, Reinhold and Taghia, Jalal and Martin, Rainer}}, booktitle = {{38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)}}, issn = {{1520-6149}}, pages = {{7477--7481}}, title = {{{Improved Single-Channel Nonstationary Noise Tracking by an Optimized MAP-based Postprocessor}}}, doi = {{10.1109/ICASSP.2013.6639116}}, year = {{2013}}, } @inproceedings{11762, abstract = {{Among the different configurations of multi-microphone systems, e.g., in applications of speech dereverberation or denoising, we consider the case without a priori information of the microphone-array geometry. This naturally invokes explicit or implicit identification of source-receiver transfer functions as an indirect description of the microphone-array configuration. However, this blind channel identification (BCI) has been difficult due to the lack of unique identifiability in the presence of observation noise or near-common channel zeros. In this paper, we study the implicit BCI performance of blind signal enhancement techniques such as the adaptive principal component analysis (PCA) or the iterative blind equalization and channel identification (BENCH). To this end, we make use of a recently proposed metric, the normalized filter-projection misalignment (NFPM), which is tailored for BCI evaluation in ill-conditioned (e.g., noisy) scenarios. The resulting understanding of implicit BCI performance can help to judge the behavior of multi-microphone speech enhancement systems and the suitability of implicit BCI to serve channel-based (i.e., channel-informed) enhancement.}}, author = {{Enzner, Gerald and Schmid, Dominic and Haeb-Umbach, Reinhold}}, booktitle = {{21th European Signal Processing Conference (EUSIPCO 2013)}}, title = {{{On the Acoustic Channel Identification in Multi-Microphone Systems via Adaptive Blind Signal Enhancement Techniques}}}, year = {{2013}}, } @inproceedings{11815, author = {{Heymann, Jahn and Walter, Oliver and Haeb-Umbach, Reinhold and Raj, Bhiksha}}, booktitle = {{Automatic Speech Recognition and Understanding Workshop (ASRU 2013)}}, title = {{{Unsupervised Word Segmentation from Noisy Input}}}, year = {{2013}}, } @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}}, } @inproceedings{11841, abstract = {{Recently, substantial progress has been made in the field of reverberant speech signal processing, including both single- and multichannel de-reverberation techniques, and automatic speech recognition (ASR) techniques robust to reverberation. To evaluate state-of-the-art algorithms and obtain new insights regarding potential future research directions, we propose a common evaluation framework including datasets, tasks, and evaluation metrics for both speech enhancement and ASR techniques. The proposed framework will be used as a common basis for the REVERB (REverberant Voice Enhancement and Recognition Benchmark) challenge. This paper describes the rationale behind the challenge, and provides a detailed description of the evaluation framework and benchmark results.}}, author = {{Kinoshita, Keisuke and Delcroix, Marc and Yoshioka, Takuya and Nakatani, Tomohiro and Habets, Emanuel and Haeb-Umbach, Reinhold and Leutnant, Volker and Sehr, Armin and Kellermann, Walter and Maas, Roland and Gannot, Sharon and Raj, Bhiksha}}, booktitle = {{ IEEE Workshop on Applications of Signal Processing to Audio and Acoustics }}, keywords = {{Reverberant speech, dereverberation, ASR, evaluation, challenge}}, pages = {{ 22--23 }}, title = {{{The reverb challenge: a common evaluation framework for dereverberation and recognition of reverberant speech}}}, year = {{2013}}, } @inproceedings{11909, abstract = {{We present a novel method to exploit correlations of adjacent time-frequency (TF)-slots for a sparseness-based blind speech separation (BSS) system. Usually, these correlations are exploited by some heuristic smoothing techniques in the post-processing of the estimated soft TF masks. We propose a different approach: Based on our previous work with one-dimensional (1D)-hidden Markov models (HMMs) along the time axis we extend the modeling to two-dimensional (2D)-HMMs to exploit both temporal and spectral correlations in the speech signal. Based on the principles of turbo decoding we solved the complex inference of 2D-HMMs by a modified forward-backward algorithm which operates alternatingly along the time and the frequency axis. Extrinsic information is exchanged between these steps such that increasingly better soft time-frequency masks are obtained, leading to improved speech separation performance in highly reverberant recording conditions.}}, author = {{Tran Vu, Dang Hai and Haeb-Umbach, Reinhold}}, booktitle = {{21th European Signal Processing Conference (EUSIPCO 2013)}}, title = {{{Blind Speech Separation Exploiting Temporal and Spectral Correlations Using Turbo Decoding of 2D-HMMs}}}, year = {{2013}}, } @inproceedings{11921, abstract = {{In this paper we consider the unsupervised word discovery from phonetic input. We employ a word segmentation algorithm which simultaneously develops a lexicon, i.e., the transcription of a word in terms of a phone sequence, learns a n-gram language model describing word and word sequence probabilities, and carries out the segmentation itself. The underlying statistical model is that of a Pitman-Yor process, a concept known from Bayesian non-parametrics, which allows for an a priori unknown and unlimited number of different words. Using a hierarchy of Pitman-Yor processes, language models of different order can be employed and nesting it with another hierarchy of Pitman-Yor processes on the phone level allows for backing off unknown word unigrams by phone m-grams. We present results on a large-vocabulary task, assuming an error-free phone sequence is given. We finish by discussing options how to cope with noisy phone sequences.}}, author = {{Walter, Oliver and Haeb-Umbach, Reinhold and Chaudhuri, Sourish and Raj, Bhiksha}}, booktitle = {{IEEE International Conference on Robotics and Automation (ICRA 2013)}}, title = {{{Unsupervised Word Discovery from Phonetic Input Using Nested Pitman-Yor Language Modeling}}}, year = {{2013}}, } @inproceedings{11924, author = {{Walter, Oliver and Korthals, Timo and Haeb-Umbach, Reinhold and Raj, Bhiksha}}, booktitle = {{Automatic Speech Recognition and Understanding Workshop (ASRU 2013)}}, title = {{{Hierarchical System for Word Discovery Exploiting DTW-Based Initialization}}}, year = {{2013}}, } @techreport{11926, abstract = {{In this paper we present a novel initialization method for unsupervised learning of acoustic patterns in recordings of continuous speech. The pattern discovery task is solved by dynamic time warping whose performance we improve by a smart starting point selection. This enables a more accurate discovery of patterns compared to conventional approaches. After graph-based clustering the patterns are employed for training hidden Markov models for an unsupervised speech acquisition. By iterating between model training and decoding in an EM-like framework the word accuracy is continuously improved. On the TIDIGITS corpus we achieve a word error rate of about 13 percent by the proposed unsupervised pattern discovery approach, which neither assumes knowledge of the acoustic units nor of the labels of the training data.}}, author = {{Walter, Oliver and Schmalenstroeer, Joerg and Haeb-Umbach, Reinhold}}, title = {{{A Novel Initialization Method for Unsupervised Learning of Acoustic Patterns in Speech (FGNT-2013-01)}}}, year = {{2013}}, } @inproceedings{15918, author = {{Jager, Tibor and Paterson, Kenneth G. and Somorovsky, Juraj}}, booktitle = {{20th Annual Network and Distributed System Security Symposium, NDSS 2013, San Diego, California, USA, February 24-27, 2013}}, title = {{{One Bad Apple: Backwards Compatibility Attacks on State-of-the-Art Cryptography}}}, year = {{2013}}, } @misc{9648, author = {{Mildorf, Jarmila}}, booktitle = {{Informationsmittel für Bibliotheken}}, title = {{{Book review: Christoph Schubert (2009). 'Raumkonstitution durch Sprache: Blickführung, Bildschemata und Kohäsion in Deskriptionssequenzen englischer Texte.' Tübingen: Max Niemeyer}}}, year = {{2013}}, } @article{28688, abstract = {{Why are nearly simultaneous stimuli frequently perceived in reversed order? The origin of errors in temporal judgments is a question older than experimental psychology itself. One of the earliest suspects is attention. According to the concept of prior entry, attention accelerates attended stimuli; thus they have “prior entry” to perceptive processing stages, including consciousness. Although latency advantages for attended stimuli have been revealed in psychophysical studies many times, these measures (e.g. temporal order judgments, simultaneity judgments) cannot test the prior-entry hypothesis completely. Since they assess latency differences between an attended and an unattended stimulus, they cannot distinguish between faster processing of attended stimuli and slower processing of unattended stimuli. Therefore, we present a novel paradigm providing separate estimates for processing advantages respectively disadvantages of attended and unattended stimuli. We found that deceleration of unattended stimuli contributes more strongly to the prior-entry illusion than acceleration of attended stimuli. Thus, in the temporal domain, attention fulfills its selective function primarily by deceleration of unattended stimuli. That means it is actually posterior entry, not prior entry which accounts for the largest part of the effect.}}, author = {{Weiß, Katharina and Hilkenmeier, Frederic and Scharlau, Ingrid}}, issn = {{1932-6203}}, journal = {{PLoS ONE}}, title = {{{Attention and the Speed of Information Processing: Posterior Entry for Unattended Stimuli Instead of Prior Entry for Attended Stimuli}}}, doi = {{10.1371/journal.pone.0054257}}, volume = {{8}}, year = {{2013}}, } @inproceedings{11832, abstract = {{In this paper we propose an approach to retrieve the absolute geometry of an acoustic sensor network, consisting of spatially distributed microphone arrays, from reverberant speech input. The calibration relies on direction of arrival measurements of the individual arrays. The proposed calibration algorithm is derived from a maximum-likelihood approach employing circular statistics. Since a sensor node consists of a microphone array with known intra-array geometry, we are able to obtain an absolute geometry estimate, including angles and distances. Simulation results demonstrate the effectiveness of the approach.}}, author = {{Jacob, Florian and Schmalenstroeer, Joerg and Haeb-Umbach, Reinhold}}, booktitle = {{38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)}}, issn = {{1520-6149}}, keywords = {{Geometry calibration, microphone arrays, position self-calibration}}, pages = {{116--120}}, title = {{{DoA-Based Microphone Array Position Self-Calibration Using Circular Statistic}}}, doi = {{10.1109/ICASSP.2013.6637620}}, year = {{2013}}, } @inproceedings{11891, abstract = {{In this paper we present a combined hardware/software approach for synchronizing the sampling clocks of an acoustic sensor network. A first clock frequency offset estimate is obtained by a time stamp exchange protocol with a low data rate and computational requirements. The estimate is then postprocessed by a Kalman filter which exploits the specific properties of the statistics of the frequency offset estimation error. In long term experiments the deviation between the sampling oscillators of two sensor nodes never exceeded half a sample with a wired and with a wireless link between the nodes. The achieved precision enables the estimation of time difference of arrival values across different hardware devices without sharing a common sampling hardware.}}, author = {{Schmalenstroeer, Joerg and Haeb-Umbach, Reinhold}}, booktitle = {{21th European Signal Processing Conference (EUSIPCO 2013)}}, keywords = {{synchronization, acoustic sensor network}}, title = {{{Sampling Rate Synchronisation in Acoustic Sensor Networks with a Pre-Trained Clock Skew Error Model}}}, year = {{2013}}, }