Automatic derivation of land-use from topographic data

The paper presents an approach for the reclassification and generalization of land-use information from topographic information. Based on a given transformation matrix describing the transition from topographic data to land-use data, a semantic and geometry based generalization of too small features for the target scale is performed. The challenges of the problem are as follows: (1) identification and reclassification of heterogeneous feature classes by local interpretation, (2) presence of concave, narrow or very elongated features, (3) processing of very large data sets. The approach is composed of several steps consisting of aggregation, feature partitioning, identification of mixed feature classes and simplification of feature outlines. The workflow will be presented with examples for generating CORINE Land Cover (CLC) features from German Authoritative Topographic Cartographic Information System (ATKIS) data for the whole are of Germany. The results will be discussed in detail, including runtimes as well as dependency of the result on the parameter setting

A fast EM algorithm for Gaussian model-based source separation

International audienceWe consider the FASST framework for audio source separation, which models the sources by full-rank spatial covariance matrices and multilevel nonnegative matrix factorization (NMF) spectra. The computational cost of the expectation-maximization (EM) algorithm in [1] greatly increases with the number of channels. We present alternative EM updates using discrete hidden variables which exhibit a smaller cost. We evaluate the results on mixtures of speech and real-world environmental noise taken from our DEMAND database. The proposed algorithm is several orders of magnitude faster and it provides better separation quality for two-channel mixtures in low input signal-to-noise ratio (iSNR) conditions

Wind Noise Reduction with a Diffusion-based Stochastic Regeneration Model

In this paper we present a method for single-channel wind noise reduction using our previously proposed diffusion-based stochastic regeneration model combining predictive and generative modelling. We introduce a non-additive speech in noise model to account for the non-linear deformation of the membrane caused by the wind flow and possible clipping. We show that our stochastic regeneration model outperforms other neural-network-based wind noise reduction methods as well as purely predictive and generative models, on a dataset using simulated and real-recorded wind noise. We further show that the proposed method generalizes well by testing on an unseen dataset with real-recorded wind noise. Audio samples, data generation scripts and code for the proposed methods can be found online (https://uhh.de/inf-sp-storm-wind).Comment: Submitted to VDE 15th ITG conference on Speech Communicatio

Customizable End-to-end Optimization of Online Neural Network-supported Dereverberation for Hearing Devices

This work focuses on online dereverberation for hearing devices using the weighted prediction error (WPE) algorithm. WPE filtering requires an estimate of the target speech power spectral density (PSD). Recently deep neural networks (DNNs) have been used for this task. However, these approaches optimize the PSD estimate which only indirectly affects the WPE output, thus potentially resulting in limited dereverberation. In this paper, we propose an end-to-end approach specialized for online processing, that directly optimizes the dereverberated output signal. In addition, we propose to adapt it to the needs of different types of hearing-device users by modifying the optimization target as well as the WPE algorithm characteristics used in training. We show that the proposed end-to-end approach outperforms the traditional and conventional DNN-supported WPEs on a noise-free version of the WHAMR! dataset.Comment: \copyright 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work

Spatial properties of the DEMAND noise recordings

National audience"DEMAND" (Diverse Environments Multichannel Acoustic Noise Database) is a set of recordings of environmental noises in both indoor and outdoor settings. The recordings were performed with a 16-channel planar array of microphones. The purpose of the recording is to provide researchers with a large set of freely available noise recordings (licensed under a Creative Commons licence) for use in developing algorithms such as beamforming, noise reduction, and source separation, although anyone may use the data for any purpose they see fit. A more detailed description of the DEMAND recordings can be found in [1]. In this article, we examine some of the spatial properties of the DEMAND recordings, in particular the cross-channel correlations. Notably, the quality of the reverberation characteristics is compared to the theoretical ideal. This property is used as a post-recording calibration of the microphone positions, compared to the design speci cations of the array

Sprecherlokalisation in Hörgeräten : Wie Hörgeräte Stimmen im Raum orten können

Die präzise räumliche Lokalisation von Sprechern im Umfeld eines Hörgerätes ist eine sehr wichtige und sehr rechenintensive Aufgabe, die die Qualität von digitalen Hörhilfen signifikant steigern kann. In diesem Beitrag wird die enge interdisziplinäre Zusammenarbeit zwischen den Entwicklern der Lokalisations-Algorithmen an der Universität Oldenburg und den Hardware-Ingenieuren der Leibniz Universität Hannover präsentiert

Statistical quality assessment and outlier detection for liquid chromatography-mass spectrometry experiments

<p>Abstract</p> <p>Background</p> <p>Quality assessment methods, that are common place in engineering and industrial production, are not widely spread in large-scale proteomics experiments. But modern technologies such as Multi-Dimensional Liquid Chromatography coupled to Mass Spectrometry (LC-MS) produce large quantities of proteomic data. These data are prone to measurement errors and reproducibility problems such that an automatic quality assessment and control become increasingly important.</p> <p>Results</p> <p>We propose a methodology to assess the quality and reproducibility of data generated in quantitative LC-MS experiments. We introduce quality descriptors that capture different aspects of the quality and reproducibility of LC-MS data sets. Our method is based on the Mahalanobis distance and a robust Principal Component Analysis.</p> <p>Conclusion</p> <p>We evaluate our approach on several data sets of different complexities and show that we are able to precisely detect LC-MS runs of poor signal quality in large-scale studies.</p

Detection, purification and identification of urotensin-II generating enzymes

Titel und Inhaltsverzeichnis Einleitung Material und Methode Ergebnisse Diskussion Zusammenfassung Summary Literaturverzeichnis Erfolgte Publikationen AnhangZiel dieser Arbeit war es, Urotensin-II-(UII)-generierende Enzyme in Extrakten aus Schweinenieren aufzuspüren, zu reinigen, und zu identifizieren. Zur Entwicklung der Reinigungsschritte ist ein chromatographisches System (PPS- System) zur schnellen Bestimmung optimaler chromatographischer Parameter für diese Arbeit entwickelt worden. Für den Nachweis der UII-generierenden Aktivität wurde das Massenspektrometrie-basierte Enzym-Screening-System (MES- System) genutzt. Mit der Kombination dieser beiden neuen Systeme ist es gelungen, weitgehend homogene Fraktionen mit UII-generierender Aktivität aus Nierengewebe zu gewinnen. In einer dieser Fraktionen konnte ein Enzym durch die MALDI-Fingerprint Analyse als "Pregnancy-Associated-Glycoprotein" (PAG2) identifiziert werden. In einer zweiten Fraktion mit UII-generierender Aktivität wurde mit der LC-ESI MS/MS Analyse eine Dislufid-Isomerase-A3 identifiziert. PAG2 zeigt im Bereich seines katalytischen Zentrums eine Sequenzhomologie mit Pepsin. Es konnte nachgewiesen werden, dass eine Pepsin- Präparation, gewonnen aus dem Magen des Schweins, UII generiert. Daher ist die Wahrscheinlichkeit groß, dass PAG2 für die Generierung von UII verantwortlich ist. Da die Disulfid-Isomerase-A3 durch eine einfache Punktmutation eine Serin-Protease-Aktivität zeigt und die gereinigte UII-generierende Fraktion durch Aprotinin, einem Serin-Protease-Inhibitor, gehemmt werden konnte, ist auch in diesem Fall die Wahrscheinlichkeit hoch, dass ein Enzym, das eine hohe Sequenzübereinstimmung mit der Disulfid-Isomerase-A3 hat, UII generiert. Inwieweit die identifizierten Enzyme die Schlüsselenzyme für die UII- Generierung darstellen, müssen zukünftige Untersuchungen zeigen. Es konnte zum ersten mal gezeigt werden, dass UII möglicherweise über die Peptide KPYKKR-UII und KR-UII aus dem Precursor generiert wird, vergleichbar der Generierung von Angiotensin-II über Angiotensin-I aus Angiotensinogen. In dieser Arbeit ist es nicht nur gelungen, PAG2, die Disulfid-Isomerase-A3 und Pepsin A als UII-generierende-Proteasen zu identifizieren sondern es konnte auch gezeigt werden, dass UII über mehrere Urotensin-Peptidvorstufen generiert werden kann.It was the aim of this work to purify and identify urotensin- II-(UII)-converting enzymes from porcine renal tissue. For this purpose, two systems, the mass-spectrometry-assisted enzyme-screening (MES) and the protein-purification-parameter-system (PPS), were developed and used. MES was used for the detection of UII-generating activity. With PPS parameters for the optimized chromatographic purification of a target protein can be estimated very fast. By applying the MES and the PPS systems nearly homogenous porcine kidney fractions with UII-generating activity were yielded. With the MALDI fingerprint method the pregnancy-associated-glycoprotein (PAG2), and with the LC-ESI MS/MS strategy the disulfide-isomerase-A3 were identified in two different fractions. The catalytic domain of PAG2 is identical with that of pepsin. Since the incubation of the urotensin substrate with pepsin from porcine stomach generates UII as the PAG2 containing fraction, it is likely, that PAG2 is responsible for the UII formation. The disulfide-isomerase-A3 can be easily converted in a serine protease by a simple point mutation. Because the UII-generating fraction was inhibited by aprotinin, a serine protease inhibitor, the propability is high, that an enzyme with a high homology towards the disulfide-isomerase-A3 has an UII-generating activity. Furthermore, in this work was demonstrated for the first time, that UII may be generated via larger peptides, KPYKKR-UII and KR-UII from its precursor, comparable to angiotensin-II, which is generated from angiotensinogen via angiotensin-I. Future work must prove, if PAG2 and the disulfide-isomerase-A3 are physiologically relevant for the UII-generation. In conclusion, PAG2, the disulfide-isomerase-A3 and pepsin A were identified, which are able to generate UII. It was shown that UII can be generated stepwise via several precursor-peptides

A sparse auditory envelope representation with iterative reconstruction for audio coding

Modern audio coding exploits the properties of the human auditory system to efficiently code speech and music signals. Perceptual domain coding is a branch of audio coding in which the signal is stored and transmitted as a set of parameters derived directly from the modeling of the human auditory system. Often, the perceptual representation is designed such that reconstruction can be achieved with limited resources but this usually means that some perceptually irrelevant information is included. In this thesis, we investigate perceptual domain coding by using a representation designed to contain only the audible information regardless of whether reconstruction can be performed efficiently. The perceptual representation we use is based on a multichannel Basilar membrane model, where each channel is decomposed into envelope and carrier components. We assume that the information in the carrier is also present in the envelopes and therefore discard the carrier components. The envelope components are sparsified using a transmultiplexing masking model and form our basic sparse auditory envelope representation (SAER). An iterative reconstruction algorithm for the SAER is presented that estimates carrier components to match the encoded envelopes. The algorithm is split into two stages. In the first, two sets ofenvelopes are generated, one of which expands the sparse envelope samples while the other provides limits for the iterative reconstruction. In the second stage, the carrier components are estimated using a synthesis-by-analysis iterative method adapted from methods designed for reconstruction from magnitude-only transform coefficients. The overall system is evaluated using subjective and objective testing on speech and audio signals. We find that some types of audio signals are reproduced very well using this method whereas others exhibit audible distortion. We conclude that, except for in some specific cases where part of the carrier information is required, most of the audible information is present in the SAER and can be reconstructed using iterative methods.Le codage audio moderne exploite les propriétés du système auditif humain de manière à coder efficacement la parole et la musique. Le codage en domaine perceptuel est une branche du codage audio dans lequel le signal est enregistré et transmis sous forme d'un ensemble de paramètres provenant directement d'un modèle du système auditif humain. La représentation perceptuelle est souvent conçue pour que la reconstruction puisse être réalisée avec des ressources limitées, mais cela requiert généralement l'inclusion de certaines informations perceptuellement non pertinentes. Dans cette thèse, nous étudions le codage perceptuel en utilisant une représentation destinée à ne contenir que l'information sonore, indépendamment du fait que la reconstruction puisse être effectuée de manière efficace. La représentation perceptuelle que nous utilisons est basée sur un modèle à canaux multiples de la membrane basilaire pour lequel chaque canal est décomposé en éléments de l'enveloppe et du signal porteur. Nous supposons que l'information contenue dans le signal porteur est également présente dans les enveloppes et supprimons donc les composantes du signal porteur. Les composantes de l'enveloppe sont réduites à l'aide d'un modèle de masquage transmultiplexeur pour former notre représentation parcimonieuse des enveloppes sonores (RPES). Nous présentons un algorithme de reconstruction itératif pour la RPES qui fait une estimation des composantes du signal porteur à partir des enveloppes codées. L'algorithme a deux étapes. À la première étape, deux ensembles d'enveloppes sont produits: le premier dilate les échantillons des enveloppes clairsemées tandis que le deuxieme fournit des limites pour la reconstruction itérative. À la deuxième étape, les éléments du signal porteur sont estimés en utilisant une méthode d'analyse par synthèse itérative adaptée de méthodes conçues pour la reconstruction de coefficients de la transformée de grandeur. Le système est évalué à l'aide de tests subjectifs et objectifs sur des signaux de parole et audio. Nous constatons que certains types de signaux audio sont très bien reproduits par cette méthode alors que d'autres démontrent de la distorsion audible. Nous concluons que, sauf dans certains cas spécifiques où une partie de l'information du signal porteur est indispensable, la majorité de l'information sonore est présente dans la RPES et peut être reconstruite en utilisant des méthodes itératives

Acoustic noise suppression for speech signals using auditory masking effects

The process of suppressing acoustic noise in audio signals, and speech signals in particular, can be improved by exploiting the masking properties of the human hearing system. These masking properties, where strong sounds make weaker sounds inaudible, are calculated using auditory models. This thesis examines both traditional noise suppression algorithms and ones that incorporate an auditory model to achieve better performance. The different auditory models used by these algorithms are examined. A novel approach, based on a method to remove a specific type of noise from audio signals, is presented using a standardized auditory model. The proposed method is evaluated with respect to other noise suppression methods in the problem of speech enhancement. It is shown that this method performs well in suppressing noise in telephone-bandwidth speech, even at low Signal-to-Noise Ratios