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Acoustic Reflector Localization and Classification

Acoustic Reflector Localization and Classification

引用

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Luca Remaggi Hansung Kim Philip J. B. Jackson Filippo Maria Fazi Adrian Hilton Centre for Vision Speech and Signal Processing University of Surrey Guildford GU2 7XH UK Institute of Sound and Vibration Research University of Southampton Southampton SO17 1BJ UK

ISBN: (纸本)9781538646595

The process of understanding acoustic properties of environments is important for several applications, such as spatial audio, augmented reality and source separation. In this paper, multichannel room impulse responses are recorded and transformed into their direction of arrival (DOA)-time domain, by employing a superdirective beamformer. This domain can be represented as a 2D image. Hence, a novel image processing method is proposed to analyze the DOA-time domain, and estimate the reflection times of arrival and DOAs. The main acoustically reflective objects are then localized. Recent studies in acoustic reflector localization usually assume the room to be free from furniture. Here, by analyzing the scattered reflections, an algorithm is also proposed to binary classify reflectors into room boundaries and interior furniture. Experiments were conducted in four rooms. The classification algorithm showed high quality performance, also improving the localization accuracy, for non-static listener scenarios.

关键词： Reflector Localization Reflector Size Classification Room Impulse Response Beamforming Beamforming reflectors sorting algorithm Acoustics two dimentional images Quality factor Source separation TIME-DOMAIN Furnishings Domain

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An Ensemble Learning Approach to Detect Epileptic Seizures from Long Intracranial EEG Recordings

An Ensemble Learning Approach to Detect Epileptic Seizures f...

引用

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： J.-B. Schiratti Jean-Eudes Le Douget Michel Le Van Quyen Slim Essid Alexandre Gramfort LTCI Telecom ParisTech Universite Paris-Saclay Paris 75013 France Bioelectrics lab Brain and Spine Institute (ICM) Paris France

ISBN: (纸本)9781538646595

This paper proposes a patient-specific supervised classification algorithm to detect seizures in long offline intracranial electroencephalographic (iEEG) recordings. The main idea of the proposed algorithm is to combine a set of probabilistic classifiers, trained on a dataset of 1 s epochs, into a weighted ensemble classifier which can be used to analyze longer 5 s data segments. The method is trained and evaluated on 24 patients, all suffering from focal medically intractable epilepsy, from the Epilepsiae database. The evaluation of the method, conducted using an average of 113 hours (min: 32 h, max: 229 h) of iEEG data per patient, shows that the proposed algorithm improves upon existing methods for seizure detection with iEEG.

关键词： Seizure detection intracranial EEG supervised learning Semi-supervised Learning epileptic seizure Electrocorticography Recordings Seizures sorting algorithm Classifiers evaluation method Dataset

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Evaluating Combinations of Classification algorithms and Paragraph Vectors for News Article Classification

Evaluating Combinations of Classification Algorithms and Par...

引用

Federated Conference on Computer Science and Information Systems

作者： Johannes Linden Stefan Forsstrom Tingting Zhang Departement of Information Systems and Technology Mid Sweden University

ISBN: (纸本)9781538623718

News companies have a need to automate and make the process of writing about popular and new events more effective. Current technologies involve robotic programs that fill in values in templates and website listeners that notify editors when changes are made so that the editor can read up on the source change on the actual website. Editors can provide news faster and better if directly provided with abstracts of the external sources and categorical meta-data that supports what the text is about. In this article, the focus is on the importance of evaluating critical parameter modifications of the four classification algorithms Decisiontree, Randomforest, Multi Layer perceptron and Long-Short-Term-Memory in a combination with the paragraph vector algorithms Distributed Memory and Distributed Bag of Words, with an aim to categorise news articles. The result shows that Decisiontree and Multi Layer perceptron are stable within a short interval, while Randomforest is more dependent on the parameters best split and number of trees. The most accurate model is Long-Short-Term-Memory model that achieves an accuracy of 71%.

关键词： news sorting algorithm Editors websites short interval multilayers Neural Networks (Computer) Writing Process Approach (Writing) Parameter modification jigs(templates)

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Operational Risk Evaluation Of Cable Plugs Using An Automated Multisensor Classification System

Operational Risk Evaluation Of Cable Plugs Using An Automate...

引用

VDE High Voltage Technology 2018;ETG-Symposium

作者： Bjoern Boettcher Ali Sinai Matthias Menge Ronald Plath Thomas Graef Thomas Huecker

ISBN: (纸本)9783800748075

The integrity of high-voltage (HV) insulation can be evaluated by measuring partial discharge (PD) activity. PD detection and monitoring is usually performed based on a single physical sensor and is susceptible for noise interference. In order to increase the evaluation reliability, an automated multi sensor expert system for PDs at cable terminations of medium-voltage gas-insulated switchgears is presented. The objective of this work is to combine data from different sensors that capture various physical quantities, i.e. sound and electrical wave to improve reliability. The introduced system includes an intelligent classification algorithm, which classifies the detected signals from an acoustic sensor and a high frequency current transformer (HFCT). The system utilizes three redundant classification algorithms, which work independently and thus make the evaluation result even more reliable. For the classification features the Fourier transformations of the PD apparent charge Q, the voltage gradient DeltaU/Deltaphi and the voltage change DeltaU between pulses are used. An L2 distance classifier examines the data of corresponding PD defect classes in respect to certain probabilities.

关键词： Risk Assessment Fourier Transform multi sensor sorting algorithm Cable terminations Taxonomy noise disturbance acoustic transducers Palladium Physically Handicapped Proportional derivative Professional Development Descriptions, Job Automation Legumes

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Exploration of Gait Parameters Affecting the Accuracy of Force Myography-Based Gait Phase Detection

Exploration of Gait Parameters Affecting the Accuracy of For...

引用

IEEE International Conference on Biomedical Robotics and Biomechatronics

作者： X. Jiang L. Tory M. Khoshnam K. H. T. Chu C. Menon Menrva Research Group Schools of Mechatronic System and Engineering Science Simon Fraser University

ISBN: (纸本)9781538681848

Gait analysis has been considered in various scenarios to provide information about the ambulatory physical activity. In this regard, studying gait phases can provide valuable information about the quality of gait. Force myography (FMG) techniques have been successfully employed to detect gait events using pattern recognition methods. This paper explores how the accuracy of detecting gait phases is correlated with the parameters of gait and FMG signal. To this end, FMG data were collected from 11 volunteers walking on a treadmill with a custom-designed FMG ankle band. The collected FMG data were classified into four gait phases using Linear Discriminant Analysis (LDA) algorithm. The correlation between the error in classification and the parameters of gait and FMG signal was then investigated. The results show that in comparison with other studied parameters, variations in stride length have the most impact on the accuracy of gait phase classification with a coefficient of determination (R~2) of 0.80. Such an effect is more pronounced when signal power-related features, such as root mean square (RMS), are used in the classification algorithm. This study provides insight into the factors affecting the accuracy of FMG-based techniques for gait analysis and is a preliminary step towards developing high performance FMG-based wearable ambulatory activity monitoring systems.

关键词： Locomotion Gait gait analysis Exploration reduced mass pattern recognition method Phase detection sorting algorithm

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Joint Gender-, Tone-, Vowel- Classification Via Novel Hierarchical Classification for Annotation of Monosyllabic Mandarin Word Tokens

Joint Gender-, Tone-, Vowel- Classification Via Novel Hierar...

引用

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Saurabh Garg Ghassan Hamarneh Allard Jongman Joan Sereno Yue Wang Pacific Parkinson's Research Centre University of British Columbia Canada Medical Image Analysis Lab Simon Fraser University (SFU) Canada KU Phonetics and Psycholinguistics Lab Department of Linguistics University of Kansas (KU) USA Language and Brain Lab Department of Linguistics Simon Fraser University Canada

ISBN: (纸本)9781538646595

The automatic annotation of Mandarin monosyllabic audio word tokens remains an important yet challenging issue in phonetics research. In this work, we address this annotation task via a novel subcategories-classification framework that not only performs word identification via the joint classifications of vowel and tone subcategories, but also performs gender discrimination of the speaker, which stands in contrast to previously proposed methods for Mandarin speech that focused only on tone-, vowel-, or gender- classification. We also propose a novel hierarchical classification algorithm to boost overall classification performance. Extensive experimental results show that our approach yielded superior performance in both cases of adequate and very limited training data. When trained using data from only one female and one male speaker, our approach also yielded the best classification accuracy in all subcategories of the token annotation problem, achieving an Fl-score of 0.742 as opposed to 0.705 as achieved by the second competing approach.

关键词： monosyllabic Mandarin tokens phonetics hierarchical classification label fusion multi-class S VMs phonetics Speech Sounds ANNOTATIONS Joints Loudspeakers Sex Discrimination token Taxonomy sorting algorithm

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Detection of Deception Using Facial Expressions Based on Different Classification algorithms

Detection of Deception Using Facial Expressions Based on Dif...

引用

Scientific Conference of Electrical Engineering (SCEE)

作者： Harith H. Thannoon Wissam H. Ali Ivan A. Hashim Department of Electrical Engineering Nineveh University Mosul Iraq Department of Electrical Engineering University of Technology-Iraq Baghdad Iraq

ISBN: (纸本)9781728115887;9781728115870

Most psychologists argue that facial behavioral during lying is different from facial behavioral when telling the truth, so the facial behavioral can be used as reliable indicators for spotting liars. The deception detection systems (DDSs) based on facial expressions are non-invasive, mobile and cost-effective. In this work a DDS is dependent on Facial Action Coding System (FACS) for facial features extraction, the main idea of FACS is to describe all facial actions using Action Units (AUs), each AU is related to movement one or more facial muscles. Eight AUs are used which incorporated into a single facial behavior pattern vector; these AUs are AUs 5, 6, 7, 10, 12, 14, 23, and 28. Datasets are collected for 43 subjects (20males, 23 females) most of them between ages 18-25. Four types of classification algorithms are used individually in the last stage of the proposed system; these classifiers are MLP, KNN, VG-RAM, and SVM. The simulation results show that the best results are obtaining when using VG-RAM and KNN classifiers. The main contributions of this work are new classification techniques in DDS, collect real database that can use to measure the performance of any DDS based on facial expressions, and select suitable facial features.

关键词： face recognition feature extraction image classification image coding multilayer perceptrons muscle nearest neighbour methods object detection random-access storage support vector machines Facial Expression random access machine Multilayer perceptrons Feature extraction Object detection Deception image coding Support Vector Network Image classification direct digital synthesis sorting algorithm Facial Recognition nearest neighbor algorithm Face Face

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Semi-Supervised Sleep-Stage Scoring Based on Single Channel EEG

Semi-Supervised Sleep-Stage Scoring Based on Single Channel ...

引用

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： A. M. Munk K. V. Olesen S. W. Gangstad L. K. Hansen Technical University of Denmark Department of Applied Mathematics and Computer Science DK-2800 Kongens Lyngby Denmark

ISBN: (纸本)9781538646595

The field of automatic sleep stage classification based on EEG has enjoyed substantial attention during the last decade, which has resulted in several supervised classification algorithms with highly encouraging performance. Such supervised machine learning algorithms require large training sets that have been manually labelled, and are time- and resource-consuming to acquire. Here we present a semi-supervised approach that can learn to distinguish the sleep stages from a one-night data set where only a fraction has been manually labelled. We show that for fractions larger than 50%, our semi-supervised approach performs as good as a similar, fully-supervised model.

关键词： EEG semi-supervised learning sleep stage scoring non-negative matrix factorizaton generalizable Gaussian mixture model Electroencephalography Semi-supervised Learning Scoring Scoring Manual handling Sleep Stages sorting algorithm

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A Review of the Basics Statistical Concepts Used in Clinical Tests Interpretation and Decision Support 44

A Review of the Basics Statistical Concepts Used in Clinical...

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44th Computing in Cardiology Conference (CinC)

作者： Wang, John Philips Healthcare MS-4308 3000 Minuteman Rd Andover MA 01810 USA

ISBN: (纸本)9781538645550;9781538666302

Numerous diagnostic tests are routinely performed clinically to: 1) screen for disease, 2) establish or rule out a diagnosis, and 3) track and monitor disease progression and effectiveness of treatment. Thus, interpretation of diagnostic test results is critical in supporting clinical decisions for the most effective patient management. The basic performance statistics required in the test results interpretation, including sensitivity (Se), specificity (Sp), overall accuracy (ACC), pretest likelihood (prevalence), and posttest likelihood including positive predictive value (PPV) and negative predictive value (NPV) are reviewed. The definitions and formulas of these performance measures and their relationships are summarized. Unlike Se and Sp, which are independent of the prevalence of the condition being tested, the other three performance measures PPV, NPV, and ACC depend on the disease prevalence. It is very important to understand the impact of disease prevalence when using these performance measures in reporting and interpreting the test results. The problem (also known as "accuracy paradox") of using the single performance measure ACC to characterize the test performance is described and explained. Despite its simplicity and frequent use in the published literatures for performance reporting, it is shown that the overall accuracy is not a reliable performance measure and its use should be avoided. The most important relationship of PPV as a function of the pretest likelihood and the accuracy of the test method (specified by Se and Sp) is clearly described and explained. With a clear understanding of the relationship between the pretest likelihood and PPV, discussion of several topics are presented to show how these basic statistical concepts can be applied in a variety of situations for effective decision support and patient management.

关键词： Sensitivity Diseases Sensitivity and specificity Classification algorithms Sociology Statistics Monitoring sorting algorithm Sociology decision making support statistical concepts Sensitivity and Specificity maladjustment p-phenyl vinyl Clinical Test Result Selenium Adaptive cruise control 1-aminocyclopropane-1-carboxylic acid Performance metrics ABNORMAL CONDITION CONTROL PRETEST Performance Tests

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Sleep Insights from the Finger Tip: How Photoplethysmography Can Help Quantify Sleep 44

Sleep Insights from the Finger Tip: How Photoplethysmography...

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44th Computing in Cardiology Conference (CinC)

作者： Eyal, Shuli Baharav, Anda HypnoCore LTD Petah Tiqwa Israel WinMed Wingate Inst Netanya Israel

ISBN: (纸本)9781538645550;9781538666302

Sleep is essential for a healthy and productive life, yet its importance is largely overlooked, allowing populations to sleep less and to develop sleep disturbances. This trend results into an epidemic of poor quality and insufficient sleep that is turn jeopardizes health, performance, mood, memory, social relationships and productivity. A first step to overcome this epidemic relies on uncovering it at the individual and societal levels. The availability of different wearable devices that can track physiological signals represents a great opportunity to define and quantify the problem. Many such devices incorporate a photoplethysmography (PPG) sensor. This triggers interest in studies aiming to get insight into human sleep structure based on information obtained from PPG sensors. This study aimed to validate a new automated sleep analysis which is simply based on the inter-beat-interval series obtained from PPG, and that uses features of heart rate variability. The candidate algorithm was tested against gold standard scoring of whole night state-of-the-art sleep studies. The PPG-based sleep scoring performs very well in differentiating sleep stages, however, the sleep/wake separation is not sufficient and requires improvement. This last task is facilitated by the fact that the majority of devices with PPG capabilities, are equipped with accelerometers providing additional information for better separation. Combining accelerometers and PPG signals from wearable devices in a sleep analyser is likely to provide a reliable and accurate automated detection of sleep and wakefulness, including sleep macro- and micro-architecture.

关键词： Sleep Standards Biomedical monitoring Gold Accelerometers Heart rate variability Classification algorithms Sleep accelerometers Biomedical monitoring heart rate variability sorting algorithm SRGN gene Photoplethysmography Gold Epidemic

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