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9th International conference on New Interfaces for Musical Expression, NIME 2009

作者： D’alessandro, Nicolas Dutoit, Thierry Circuit Theory and Signal Processing Lab Faculty of Engineering Mons [Polytech.Mons 31 Dolez Boulevard Mons7000 Belgium

In this paper we present new issues and challenges related to the vertical tablet playing. The approach is based on a previously presented instrument, the HANDSKETCH. This instrument has now been played regularly for more than two years by several performers. Therefore this is an opportunity to propose a better understanding of the performing strategy. We present the behavior of the whole body as an underlying aspect in the manipulation of the instrument. © 2009 Steering Committee of the International Conference on New Interfaces for Musical Expression. All rights reserved.

关键词： Music

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Effects of channel estimation error on array processing based QO-STBC coded OFDM systems

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IEEE Communications Letters 2009年第4期13卷 212-214页

作者： Zhang, Haixia Yuan, Dongfeng Chen, Hsiao-Hwa Nossek, Josef A. WMCT Lab. Shandong University Shadong China Department of Engineering Science National Cheng Kung University Tainan City 70101 Taiwan Institute for Circuit Theory and Signal Processing Munich University of Technology Munich Germany

This letter addresses the issues on performance degradation caused by channel estimation error of quasiorthogonal space-time block (QO-STBC) coded OFDM systems employing array processing decoder. The Least Square (LS) channel estimator is employed to obtain the required channel state information (CSI). Taking mean square error (MSE) as a performance metric, we evaluate the performance of the LS estimator in MIMO-OFDM systems over frequency selective channels, and its impact on symbol error rate (SER) using both analysis and simulations. © 2009 IEEE.

关键词： Channel state information

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Fiber nonlinearity mitigation via the parzen window classifier for dispersion managed and unmanaged links 21

Fiber nonlinearity mitigation via the parzen window classifi...

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21st International Conference on Transparent Optical Networks, ICTON 2019

作者： Amari, Abdelkerim Lin, Xiang Dobre, Octavia A. Venkatesan, Ramachandran Alvarado, Alex Information and Communication Theory Lab Signal Processing Systems Group Department of Electrical Engineering Eindhoven University of Technology Netherlands Faculty of Engineering and Applied Science Memorial University St. John's Canada

ISBN: (纸本)9781728127798

Machine learning techniques have recently received significant attention as promising approaches to deal with the optical channel impairments, and in particular, the nonlinear effects. In this work, a machine learning-based classification technique, known as the Parzen window (PW) classifier, is applied to mitigate the nonlinear effects in the optical channel. The PW classifier is used as a detector with improved nonlinear decision boundaries more adapted to the nonlinear fiber channel. Performance improvement is observed when applying the PW in the context of dispersion managed and dispersion unmanaged systems. © 2019 IEEE.

关键词： Nonlinear optics

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Noise and speech estimation as auxiliary tasks for robust speech recognition 5th

Noise and speech estimation as auxiliary tasks for robust sp...

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5th International Conference on Statistical Language and Speech processing, SLSP 2017

作者： Pironkov, Gueorgui Dupont, Stéphane Wood, Sean U. N. Dutoit, Thierry Circuit Theory and Signal Processing Lab University of Mons Boulevard Dolez 31 Mons7000 Belgium NECOTIS Department of Electrical and Computer Engineering University of Sherbrooke 2500 Boulevard de l’Université SherbrookeQCJ1K 2R1 Canada

ISBN: (纸本)9783319684550

Dealing with noise deteriorating the speech is still a major problem for automatic speech recognition. An interesting approach to tackle this problem consists of using multi-task learning. In this case, an efficient auxiliary task is clean-speech generation. This auxiliary task is trained in addition to the main speech recognition task and its goal is to help improve the results of the main task. In this paper, we investigate this idea further by generating features extracted directly from the audio file containing only the noise, instead of the clean-speech. After demonstrating that an improvement can be obtained through this multi-task learning auxiliary task, we also show that using both noise and clean-speech estimation auxiliary tasks leads to a 4% relative word error rate improvement in comparison to the classic single-task learning on the CHiME4 dataset. © Springer International Publishing AG 2017.

关键词： Speech recognition

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A machine learning-based detection technique for optical fiber nonlinearity mitigation

arXiv

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arXiv 2019年

作者： Amari, Abdelkerim Lin, Xiang Dobre, Octavia A. Venkatesan, Ramachandran Alvarado, Alex Faculty of Engineering and Applied Science Memorial University St. John's Canada Information and Communication Theory Lab Signal Processing Systems Group Department of Electrical Engineering Eindhoven University of Technology Netherlands

We investigate the performance of a machine learning classification technique, called the Parzen window, to mitigate the fiber nonlinearity in the context of dispersion managed and dispersion unmanaged systems. The technique is applied for detection at the receiver side, and deals with the non-Gaussian nonlinear effects by designing improved decision boundaries. We also propose a two-stage mitigation technique using digital back propagation and Parzen window for dispersion unmanaged systems. In this case, digital back propagation compensates for the deterministic nonlinearity and the Parzen window deals with the stochastic nonlinear signal-noise interactions, which are not taken into account by digital back propagation. A performance improvement up to 0.4 dB in terms of Q factor is observed. Copyright © 2019, The Authors. All rights reserved.

关键词： Backpropagation

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Fiber nonlinearity mitigation via the parzen window classifier for dispersion managed and unmanaged links

arXiv

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arXiv 2019年

关键词： Machine learning

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Federated learning for secure development of AI models for Parkinson’s disease detection using speech from different languages

arXiv

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arXiv 2023年

作者： Arasteh, Soroosh Tayebi Rios-Urrego, Cristian David Noeth, Elmar Maier, Andreas Yang, Seung Hee Rusz, Jan Orozco-Arroyave, Juan Rafael Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Speech & Language Processing Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Department of Diagnostic and Interventional Radiology University Hospital RWTH Aachen Aachen Germany GITA Lab Faculty of Engineering University of Antioquia Medellín Colombia Department of Circuit Theory Czech Technical University in Prague Prague Czech Republic

Parkinson’s disease (PD) is a neurological disorder impacting a person’s speech. Among automatic PD assessment methods, deep learning models have gained particular interest. Recently, the community has explored cross-pathology and cross-language models which can improve diagnostic accuracy even further. However, strict patient data privacy regulations largely prevent institutions from sharing patient speech data with each other. In this paper, we employ federated learning (FL) for PD detection using speech signals from 3 real-world language corpora of German, Spanish, and Czech, each from a separate institution. Our results indicate that the FL model outperforms all the local models in terms of diagnostic accuracy, while not performing very differently from the model based on centrally combined training sets, with the advantage of not requiring any data sharing among collaborators. This will simplify inter-institutional collaborations, resulting in enhancement of patient outcomes. © 2023, CC BY-SA.

关键词： Deep learning

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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