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作者： Engelhardt, Erik Zaman, Adrian Elzenheimer, Eric Frey, Norbert Schmidt, Gerhard Digital Signal Processing and System Theory Institute of Electrical Engineering and Information Technology Faculty of Engineering Kiel University Kaiserstr. 2 Kiel24143 Germany Medical Department Iii Specialised in Cardiology and Angiology University Medical Center Schleswig-Holstein Arnold-Heller-Str. 3/House K3 Kiel24105 Germany Internal Medicine III: Heart Vascular and Lung University Medical Center Heidelberg Im Neuenheimer Feld 410 Heidelberg69120 Germany

While metrics such as SNR (signal-to-noise ratio) and ASC (application-specific capacity) can be used to describe signal quality quantitatively, they do not allow qualitative assessment. If only the SNR of a signal is given, it is not clear for which applications it is suitable. The subjective evaluation of signals by application experts, on the other hand, can provide such a qualitative assessment, but it is not reproducible. In this study, we investigate the relationship between these easily computable metrics such as SNR and ASC and the subjective evaluation of cardiologists using MCG signals as an example. To do this, we define four quality classes and generate a collection of noisy prototype signals. The cardiologists' assessments are then compared with the SNR and ASC of each signal. Due to the small amount of data, we only use a linear regression to predict quality classes based on common quantitative metrics. The achieved coefficients of determination are RSNR 2 = 0.612 and RASC 2 = 0.593. In perspective, it is therefore plausible to analytically calculate the quality class of a MCG sensor system from the system characteristics with the support of a larger cohort of cardiologists. This will allow sensor system manufacturers to optimize their systems more efficiently for specific applications, enabling faster innovation loops at lower cost. For users of sensor systems, it will simplify the selection of the most suitable system for their application. © 2022 The Author(s), published by De Gruyter.

关键词： signal to noise ratio

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Low Noise Inverse Magnetoelectric Magnetic Field Sensor

arXiv

引用

arXiv 2023年

作者： Thormählen, Lars Hayes, P. Elzenheimer, E. Spetzler, E. McCord, J. Schmidt, G. Höft, M. Meyners, D. Quandt, E. Inorganic Functional Materials Institute for Materials Science Kiel University Kiel24143 Germany Digital Signal Processing and System Theory Institute of Electrical Engineering and Information Technology Kiel University Kiel24143 Germany Microwave Engineering Institute of Electrical Engineering and Information Technology Kiel University Kiel24143 Germany Nanoscale Magnetic Materials Institute for Materials Science Kiel University Kiel24143 Germany

In the development of any type of magnetic field sensor based on magnetic films, special consideration must be given to the magnetic layer component. The presented work investigates the use of flux closing magnetostrictive multilayers for inverse magnetoelectric sensors. In such a type of magnetic field sensor, highly sensitive AC and DC field detection relies on strong excitation of the incorporated magnetic layers by piezoelectrically driven cantilever oscillation at mechanical resonances. The provoked periodic flux change is influenced by the magnetic field to be measured and is picked up by a coil, which generates the measured output. The effect of the magnetic multilayer on linearity, noise behavior, and detection limit of DC and AC signals is investigated. This study demonstrates the next step for inverse magnetoelectric thin film sensors, which achieve one order of magnitude improved detection limits with less than 8 pT/Hz1/2 at 10 Hz and 18 pT/Hz1/2 at DC using exchange bias stabilized magnetic multilayers for obtaining flux closure. Copyright © 2023, The Authors. All rights reserved.

关键词： Magnetic fields

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Voice quality alteration and its potential implications for voice therapy

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The Journal of the Acoustical Society of America 2023年第4_SUPPLEMENT期154卷 A352-A352页

作者： Isabel S. Schiller Karolin Krüger Firdous Bin Ismail Sabine J Schlittmeier Gerhard Schmidt Work and Eng. Psych. Inst. of Psych. RWTH Aachen Univ. Jaegerstrasse 17-19 Aachen 52066 Germany isabel.schiller@psych.rwth-aachen.de Digital Signal Processing and System Theory Dept. of Elec. and Information Eng. Christian-Albrechts-Univ. Kiel Germany Work and Eng. Psych. Inst. of Psych. RWTH Aachen Univ. Aachen Germany Digital Signal Processing and System Theory Dept. of Elec. and Information Eng. Christian-Albrechts-Univ. Schmidt Germany

Modulating a speaker’s auditory feedback is a valuable technique for investigating vocal motor control. In auditory feedback alteration (AFA) experiments, participants receive real-time perturbed feedback of their voice through earphones while producing vocalizations into a microphone. Previous studies primarily focused on pitch and amplitude alterations, which typically result in compensatory vocal responses. The purpose of this study is to present a voice resynthesis system, called VQ-Synth, designed for real-time auditory feedback alteration of the speaker’s voice quality in terms of hoarseness. While an initial version of VQ-Synth was implemented using Matlab, the system has now been enhanced in a real-time framework written in ANSI-C to achieve effective voice quality resynthesis with minimal delay. Additionally, a graphical user interface (GUI) was implemented to guide participants through the experiment. This work presents the technical architecture of VQ-Synth and its intended application in AFA experiments involving both healthy and dysphonic speakers. Furthermore, we discuss the potential implications of the system in the context of auditory cognition and voice therapy. We hope that the novel capabilities of VQ-Synth open up exciting possibilities for investigating how alterations in voice quality affect vocal motor control and how this knowledge can be applied in therapeutic settings.

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P 130—The influence of different settings on accuracies of gait algorithms

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Gait and Posture 2018年第Sup1期65卷 454-455页

作者： Warmerdam, E. Pham, M.H. Hansen, C. Maetzler, W. Christian-Albrechts-University Neurology Kiel Germany Christian-Albrechts-University Digital Signal Processing and System Theory Kiel Germany

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Magnetoneurograhy of an electrically stimulated arm nerve: Usability of magnetoelectric (ME) sensors for magnetic measurements of peripheral arm nerves

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Current Directions in Biomedical Engineering 2018年第1期4卷 363-366页

作者： Elzenheimer, Eric Laufs, Helmut Sander, Tilmann Schmidt, Gerhard Kiel University Digital Signal Processing and System Theory Kaiserstr. 2 Kiel 24143 Germany UKSH Department of Neurology Arnold-Heller-Str. 3 Kiel 24105 Germany Physikalisch-Technische Bundesanstalt (PTB) Abbestr. 2-12 Berlin 10587 Germany

For further development and optimization of novel uncooled magnetoelectric (ME) sensors, a better understanding of spectral power distribution and signal strength of nerve signals is of high interest. For obtaining information on these signal properties, Superconducting Quantum Interference Device (SQUID) measurements were performed at the Physikalisch-Technische Bundesanstalt (PTB) in Berlin. signal amplitudes were subject-dependent and ranged from 17 fT to 60 fT in a frequency range from 100 Hz to 1 kHz. The required SQUID averaging time was in the range of minutes, while for current ME sensors significantly longer averaging times are expected to be necessary. © 2018 Eric Elzenheimer et al.

关键词： SQUIDs

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Discriminative Training of Deep Regression Networks for Artificial Bandwidth Extension

Discriminative Training of Deep Regression Networks for Arti...

引用

International Workshop on Acoustic signal Enhancement (IWAENC)

作者： Jonas Sautter Friedrich Faubel Markus Buck Gerhard Schmidt Nuance Communications Speech Signal Enhancement Ulm Germany Kiel University Digital Signal Processing and System Theory Kiel Germany

Artificial bandwidth extension reconstructs a 16 kHz wideband signal from a given 8 kHz narrowband signal. State-of-the-art approaches use regression deep neural networks (DNNs) for extending the spectral envelope. As a cost function during training, they use the mean squared error (MSE) between true and estimated wideband spectral envelopes. With pure MSE training, the extension for fricatives and vowels is not distinctive enough compared to the true WB data. In this work, we propose to add a discriminative term to the cost function that forces the DNN to extend the energy more distinctively for different phoneme classes. The proposed cost function improves the separation of fricatives and vowels in the DNN. It also results in a higher speech quality, which was shown in subjective listening tests.

关键词： Training Cost function Bandwidth Speech processing Conferences Acoustics Neural networks

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Magnetoneurograhy of an electrically stimulated arm nerve: Usability of magnetoelectric (ME) sensors for magnetic measurements of peripheral arm nerves

Magnetoneurograhy of an electrically stimulated arm nerve: U...

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作者： Elzenheimer, Eric Laufs, Helmut Sander, Tilmann Schmidt, Gerhard Kiel University Digital Signal Processing and System Theory Kaiserstr. 2 Kiel24143 Germany UKSH Department of Neurology Arnold-Heller-Str. 3 Kiel24105 Germany Abbestr. 2-12 Berlin10587 Germany

关键词： SQUIDs

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Adversarial inpainting of medical image modalities

arXiv

引用

arXiv 2018年

作者： Armanious, Karim Mecky, Youssef Gatidis, Sergios Yang, Bin University of Stuttgart Institute of Signal Processing and System Theory Stuttgart Germany University of Tübingen Department of Radiology Tübingen Germany German University in Cairo Faculty of Information Engineering and Technology Cairo Egypt

Numerous factors could lead to partial deteriorations of medical images. For example, metallic implants will lead to localized perturbations in MRI scans. This will affect further post-processing tasks such as attenuation correction in PET/MRI or radiation therapy planning. In this work, we propose the inpainting of medical images via Generative Adversarial Networks (GANs). The proposed framework incorporates two patch-based discriminator networks with additional style and perceptual losses for the inpainting of missing information in realistically detailed and contextually consistent manner. The proposed framework outperformed other natural image inpainting techniques both qualitatively and quantitatively on two different medical modalities. Copyright © 2018, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Fully Automated and Standardized Segmentation of Adipose Tissue Compartments by Deep Learning in Three-dimensional Whole-body MRI of Epidemiological Cohort Studies

arXiv

引用

arXiv 2020年

作者： Küstner, Thomas Hepp, Tobias Fischer, Marc Schwartz, Martin Fritsche, Andreas Häring, Hans-Ulrich Nikolaou, Konstantin Bamberg, Fabian Yang, Bin Schick, Fritz Gatidis, Sergios Machann, Jürgen University Hospital Tübingen Diagnostic and Interventional Radiology Medical Image and Data Analysis Tübingen Germany University of Stuttgart Department for Signal Processing and System Theory Stuttgart Germany School of Biomedical Engineering & Imaging Sciences King’s College London St. Thomas’ Hospital London United Kingdom Max-Planck Institute for Intelligent Systems Empirical Inference Department Tübingen Germany University Hospital Tübingen Diagnostic and Interventional Radiology Section on Experimental Radiology Tübingen Germany Department of Internal Medicine IV Eberhard Karls University Tübingen Germany Helmholtz Zentrum München University of Tübingen Tübingen Germany Germany Department of Diagnostic and Interventional Radiology Medical Center – University of Freiburg Faculty of Medicine University of Freiburg Germany

Purpose: To enable fast and reliable assessment of subcutaneous and visceral adipose tissue compartments derived from whole-body MRI. Methods: Quantification and localization of different adipose tissue compartments from whole-body MR images is of high interest to examine metabolic conditions. For correct identification and phenotyping of individuals at increased risk for metabolic diseases, a reliable automatic segmentation of adipose tissue into subcutaneous and visceral adipose tissue is required. In this work we propose a 3D convolutional neural network (DCNet) to provide a robust and objective segmentation. In this retrospective study, we collected 1000 cases (66±13 years;523 women) from the Tuebingen Family Study and from the German Center for Diabetes research (TUEF/DZD), as well as 300 cases (53±11 years;152 women) from the German National Cohort (NAKO) database for model training, validation, and testing with a transfer learning between the cohorts. These datasets had variable imaging sequences, imaging contrasts, receiver coil arrangements, scanners and imaging field strengths. The proposed DCNet was compared against a comparable 3D UNet segmentation in terms of sensitivity, specificity, precision, accuracy, and Dice overlap. Results: Fast (5-7seconds) and reliable adipose tissue segmentation can be obtained with high Dice overlap (0.94), sensitivity (96.6%), specificity (95.1%), precision (92.1%) and accuracy (98.4%) from 3D whole-body MR datasets (field of view coverage 450x450x2000mm3). Segmentation masks and adipose tissue profiles are automatically reported back to the referring physician. Conclusion: Automatic adipose tissue segmentation is feasible in 3D whole-body MR data sets and is generalizable to different epidemiological cohort studies with the proposed DCNet. Copyright © 2020, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Parkinson-speech analysis: Methods and aims

Parkinson-speech analysis: Methods and aims

引用

12. ITG-Fachtagung Sprachkommunikation - 12th ITG Conference on Speech Communication

作者： Baasch, Christin Schmidt, Gerhard Heute, Ulrich Nebel, Adelheid Deuschl, Günther Digital Signal Processing and System Theory Christian-Albrechts-Universität zu Kiel Kiel24143 Germany

ISBN: (纸本)9783800742752

Parkinson's Disease (PD) is one of the most frequent neurodegenerative diseases worldwide. Besides motor disorders, patients affected by this disease mostly suffer from a speech disorder named dysarthria. It is treated by different speech therapies. As a part of that, it is practice to observe the progress of the patients' dysarthria by classifying its severity by one of different possible scales like the National Technical Institute of the Deaf (NTID) scale. These ratings are currently done by auditive assessments, which are very time consuming and expensive. This work presents the conception of a tool for acoustical analysis of Parkinson speech and classification of the severity of the speech disorder, with the goal to improve the success of the speech therapies and simplify the classification. © 2016 VDE VERLAG GMBH.

关键词： Neurodegenerative diseases

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