TY - GEN AU - Nofen, Barbara ID - 10720 TI - Verbesserung der Erkennungsrate eines Systems zur Klassifikation von EMG-Signalen durch den Einsatz eines hybriden Lagesensors ER - TY - GEN AU - Pudelko, Daniel ID - 10727 TI - Überquerung der Styx - Betriebsparametervariation und Fehlerverhalten eines Platform FPGAs ER - TY - GEN AU - Riebler, Heinrich ID - 10730 TI - Identifikation und Wiederherstellung von kryptographischen Schlüsseln mit FPGAs ER - TY - GEN AU - Sprenger, Alexander ID - 10741 TI - MiBenchHybrid : Erweiterung eines Benchmarks um Hardwarebeschleunigung ER - TY - GEN AU - Steppeler, Philipp ID - 10743 TI - Beschleunigung von Einzelbild-Erkennungsverfahren auf Datenfluss basierenden HPC Systemen ER - TY - CONF AU - Toebermann, Christian AU - Geibel, Daniel AU - Hau, Manuel AU - Brandl, Ron AU - Kaufmann, Paul AU - Ma, Chenjie AU - Braun, Martin AU - Degner, Tobias ID - 10745 T2 - Real-Time Conference TI - Real-Time Simulation of Distribution Grids with high Penetration of Regenerative and Distributed Generation ER - TY - CONF AU - Ghasemzadeh Mohammadi, Hassan AU - Gaillardon, Pierre-Emmanuel AU - Yazdani, Majid AU - De Micheli, Giovanni ID - 10774 T2 - 2013 IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFTS) TI - A fast TCAD-based methodology for Variation analysis of emerging nano-devices ER - TY - CONF AU - Gaillardon, Pierre-Emmanuel AU - Ghasemzadeh Mohammadi, Hassan AU - De Micheli, Giovanni ID - 10775 T2 - 2013 14th Latin American Test Workshop-LATW TI - Vertically-stacked silicon nanowire transistors with controllable polarity: A robustness study ER - TY - CONF AB - Whenever huge amounts of XML data have to be transferred from a web server to multiple clients, the transferred data volumes can be reduced significantly by sending compressed XML instead of plain XML. Whenever applications require querying a compressed XML format and XML compression or decompression time is a bottleneck, parallel XML compression and parallel decompression may be of significant advantage. We choose the XML compressor XSDS as starting point for our new approach to parallel compression and parallel decompression of XML documents for the following reasons. First, XSDS generally reaches stronger compression ratios than other compressors like gzip, bzip2, and XMill. Second, in contrast to these compressors, XSDS not only supports XPath queries on compressed XML data, but also XPath queries can be evaluated on XSDS compressed data even faster than on uncompressed XML. We propose a String-search-based parsing approach to parallelize XML compression with XSDS, and we show that we can speed-up the compression of XML documents by a factor of 1.4 and that we can speed-up the decompression time even by a factor of up to 7 on a quad-core processor. AU - Böttcher, Stefan AU - Feldotto, Matthias AU - Hartel, Rita ID - 1093 T2 - WEBIST 2013 - Proceedings of the 9th International Conference on Web Information Systems and Technologies, Aachen, Germany, 8-10 May, 2013 TI - Schema-based Parallel Compression and Decompression of XML Data ER - TY - CONF AB - The accuracy of automatic speech recognition systems in noisy and reverberant environments can be improved notably by exploiting the uncertainty of the estimated speech features using so-called uncertainty-of-observation techniques. In this paper, we introduce a new Bayesian decision rule that can serve as a mathematical framework from which both known and new uncertainty-of-observation techniques can be either derived or approximated. The new decision rule in its direct form leads to the new significance decoding approach for Gaussian mixture models, which results in better performance compared to standard uncertainty-of-observation techniques in different additive and convolutive noise scenarios. AU - Abdelaziz, Ahmed H. AU - Zeiler, Steffen AU - Kolossa, Dorothea AU - Leutnant, Volker AU - Haeb-Umbach, Reinhold ID - 11716 KW - Bayes methods KW - Gaussian processes KW - convolution KW - decision theory KW - decoding KW - noise KW - reverberation KW - speech coding KW - speech recognition KW - Bayesian decision rule KW - GMM KW - Gaussian mixture models KW - additive noise scenarios KW - automatic speech recognition systems KW - convolutive noise scenarios KW - decoding approach KW - mathematical framework KW - reverberant environments KW - significance decoding KW - speech feature estimation KW - uncertainty-of-observation techniques KW - Hidden Markov models KW - Maximum likelihood decoding KW - Noise KW - Speech KW - Speech recognition KW - Uncertainty KW - Uncertainty-of-observation KW - modified imputation KW - noise robust speech recognition KW - significance decoding KW - uncertainty decoding SN - 1520-6149 T2 - Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on TI - GMM-based significance decoding ER - TY - CONF AB - 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. AU - Chinaev, Aleksej AU - Haeb-Umbach, Reinhold ID - 11740 KW - Gaussian noise KW - maximum likelihood estimation KW - parameter estimation KW - GMM parameter KW - Gaussian mixture model KW - MAP estimation KW - Map-based estimation KW - maximum a-posteriori estimation KW - maximum likelihood technique KW - noisy observation KW - sequential estimation framework KW - white Gaussian noise KW - Additive noise KW - Gaussian mixture model KW - Maximum likelihood estimation KW - Noise measurement KW - Gaussian mixture model KW - Maximum a posteriori estimation KW - Maximum likelihood estimation SN - 1520-6149 T2 - 38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013) TI - MAP-based Estimation of the Parameters of a Gaussian Mixture Model in the Presence of Noisy Observations ER - TY - CONF AB - 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. AU - Chinaev, Aleksej AU - Haeb-Umbach, Reinhold AU - Taghia, Jalal AU - Martin, Rainer ID - 11742 SN - 1520-6149 T2 - 38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013) TI - Improved Single-Channel Nonstationary Noise Tracking by an Optimized MAP-based Postprocessor ER - TY - CONF AB - 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. AU - Enzner, Gerald AU - Schmid, Dominic AU - Haeb-Umbach, Reinhold ID - 11762 T2 - 21th European Signal Processing Conference (EUSIPCO 2013) TI - On the Acoustic Channel Identification in Multi-Microphone Systems via Adaptive Blind Signal Enhancement Techniques ER - TY - CONF AU - Heymann, Jahn AU - Walter, Oliver AU - Haeb-Umbach, Reinhold AU - Raj, Bhiksha ID - 11815 T2 - Automatic Speech Recognition and Understanding Workshop (ASRU 2013) TI - Unsupervised Word Segmentation from Noisy Input ER - TY - CONF AB - 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. AU - Hoang, Manh Kha AU - Haeb-Umbach, Reinhold ID - 11816 KW - Gaussian processes KW - Global Positioning System KW - convergence KW - expectation-maximisation algorithm KW - fingerprint identification KW - indoor radio KW - signal classification KW - wireless LAN KW - EM algorithm KW - ML estimation KW - WiFi indoor positioning KW - censored Gaussian data classification KW - clipped data KW - convergence properties KW - expectation maximization algorithm KW - fingerprinting method KW - maximum likelihood estimation KW - optimal classification KW - parameters estimation KW - portable devices sensitivity KW - signal strength measurements KW - wireless LAN positioning systems KW - Convergence KW - IEEE 802.11 Standards KW - Maximum likelihood estimation KW - Parameter estimation KW - Position measurement KW - Training KW - Indoor positioning KW - censored data KW - expectation maximization KW - signal strength KW - wireless LAN SN - 1520-6149 T2 - 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013) TI - Parameter estimation and classification of censored Gaussian data with application to WiFi indoor positioning ER - TY - CONF AB - 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. AU - Kinoshita, Keisuke AU - Delcroix, Marc AU - Yoshioka, Takuya AU - Nakatani, Tomohiro AU - Habets, Emanuel AU - Haeb-Umbach, Reinhold AU - Leutnant, Volker AU - Sehr, Armin AU - Kellermann, Walter AU - Maas, Roland AU - Gannot, Sharon AU - Raj, Bhiksha ID - 11841 KW - Reverberant speech KW - dereverberation KW - ASR KW - evaluation KW - challenge T2 - IEEE Workshop on Applications of Signal Processing to Audio and Acoustics TI - The reverb challenge: a common evaluation framework for dereverberation and recognition of reverberant speech ER - TY - JOUR AB - In this contribution we extend a previously proposed Bayesian approach for the enhancement of reverberant logarithmic mel power spectral coefficients for robust automatic speech recognition to the additional compensation of background noise. A recently proposed observation model is employed whose time-variant observation error statistics are obtained as a side product of the inference of the a posteriori probability density function of the clean speech feature vectors. Further a reduction of the computational effort and the memory requirements are achieved by using a recursive formulation of the observation model. The performance of the proposed algorithms is first experimentally studied on a connected digits recognition task with artificially created noisy reverberant data. It is shown that the use of the time-variant observation error model leads to a significant error rate reduction at low signal-to-noise ratios compared to a time-invariant model. Further experiments were conducted on a 5000 word task recorded in a reverberant and noisy environment. A significant word error rate reduction was obtained demonstrating the effectiveness of the approach on real-world data. AU - Leutnant, Volker AU - Krueger, Alexander AU - Haeb-Umbach, Reinhold ID - 11862 IS - 8 JF - IEEE Transactions on Audio, Speech, and Language Processing KW - Bayes methods KW - compensation KW - error statistics KW - reverberation KW - speech recognition KW - Bayesian feature enhancement KW - background noise KW - clean speech feature vectors KW - compensation KW - connected digits recognition task KW - error statistics KW - memory requirements KW - noisy reverberant data KW - posteriori probability density function KW - recursive formulation KW - reverberant logarithmic mel power spectral coefficients KW - robust automatic speech recognition KW - signal-to-noise ratios KW - time-variant observation KW - word error rate reduction KW - Robust automatic speech recognition KW - model-based Bayesian feature enhancement KW - observation model for reverberant and noisy speech KW - recursive observation model TI - Bayesian Feature Enhancement for Reverberation and Noise Robust Speech Recognition VL - 21 ER - TY - CONF AB - 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. AU - Tran Vu, Dang Hai AU - Haeb-Umbach, Reinhold ID - 11909 T2 - 21th European Signal Processing Conference (EUSIPCO 2013) TI - Blind Speech Separation Exploiting Temporal and Spectral Correlations Using Turbo Decoding of 2D-HMMs ER - TY - CONF AB - In this paper we present a speech presence probability (SPP) estimation algorithmwhich exploits both temporal and spectral correlations of speech. To this end, the SPP estimation is formulated as the posterior probability estimation of the states of a two-dimensional (2D) Hidden Markov Model (HMM). We derive an iterative algorithm to decode the 2D-HMM which is based on the turbo principle. The experimental results show that indeed the SPP estimates improve from iteration to iteration, and further clearly outperform another state-of-the-art SPP estimation algorithm. AU - Vu, Dang Hai Tran AU - Haeb-Umbach, Reinhold ID - 11917 KW - correlation methods KW - estimation theory KW - hidden Markov models KW - iterative methods KW - probability KW - spectral analysis KW - speech processing KW - 2D HMM KW - SPP estimates KW - iterative algorithm KW - posterior probability estimation KW - spectral correlation KW - speech presence probability estimation KW - state-of-the-art SPP estimation algorithm KW - temporal correlation KW - turbo principle KW - two-dimensional hidden Markov model KW - Correlation KW - Decoding KW - Estimation KW - Iterative decoding KW - Noise KW - Speech KW - Vectors SN - 1520-6149 T2 - 38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013) TI - Using the turbo principle for exploiting temporal and spectral correlations in speech presence probability estimation ER - TY - CONF AB - 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. AU - Walter, Oliver AU - Haeb-Umbach, Reinhold AU - Chaudhuri, Sourish AU - Raj, Bhiksha ID - 11921 T2 - IEEE International Conference on Robotics and Automation (ICRA 2013) TI - Unsupervised Word Discovery from Phonetic Input Using Nested Pitman-Yor Language Modeling ER -