2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP)最新文献_第10页

A Comparison of Boosted Deep Neural Networks for Voice Activity Detection 增强深度神经网络语音活动检测的比较

2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP) Pub Date : 2019-11-01 DOI: 10.1109/GlobalSIP45357.2019.8969258

Harshit Krishnakumar, D. Williamson

{"title":"A Comparison of Boosted Deep Neural Networks for Voice Activity Detection","authors":"Harshit Krishnakumar, D. Williamson","doi":"10.1109/GlobalSIP45357.2019.8969258","DOIUrl":"https://doi.org/10.1109/GlobalSIP45357.2019.8969258","url":null,"abstract":"Voice activity detection (VAD) is an integral part of speech processing for real world problems, and a lot of work has been done to improve VAD performance. Of late, deep neural networks have been used to detect the presence of speech and this has offered tremendous gains. Unfortunately, these efforts have been either restricted to feed-forward neural networks that do not adequately capture frequency and temporal correlations, or the recurrent architectures have not been adequately tested in noisy environments. In this paper, we investigate different neural network configurations for voice activity detection. More specifically, we explore solutions that incorporate multi-resolution stacking and ensemble learning using convolutional, long short-term memory (LSTM), and dilated convolutional neural network architectures. We evaluate our approach using various speech signals that are captured in different amounts of noise. Our results show that a multi-resolution ensemble approach using LSTM recurrent neural networks performs best. This is demonstrated for seen and unseen testing scenarios.","PeriodicalId":221378,"journal":{"name":"2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125641173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Joint Angle And Delay Estimation (Jade) By Partial Relaxation 部分松弛的关节角和延时估计(玉)

2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP) Pub Date : 2019-11-01 DOI: 10.1109/GlobalSIP45357.2019.8969218

Ahmad Bazzi

引用次数: 3

Estimating Public Speaking Anxiety from Speech Signals Using Unsupervised Transfer Learning 利用无监督迁移学习从语音信号中估计公共演讲焦虑

2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP) Pub Date : 2019-11-01 DOI: 10.1109/GlobalSIP45357.2019.8969502

Kexin Feng, Megha Yadav, Md. Nazmus Sakib, A. Behzadan, Theodora Chaspari

{"title":"Estimating Public Speaking Anxiety from Speech Signals Using Unsupervised Transfer Learning","authors":"Kexin Feng, Megha Yadav, Md. Nazmus Sakib, A. Behzadan, Theodora Chaspari","doi":"10.1109/GlobalSIP45357.2019.8969502","DOIUrl":"https://doi.org/10.1109/GlobalSIP45357.2019.8969502","url":null,"abstract":"Public speaking anxiety (PSA) ranks as a top social phobia across the world caused by various confounding factors. Motivated by the inherent data sparsity and lack of annotations in human-related applications, we propose unsupervised learning techniques to estimate PSA from speech signals. The labeled source data come from the publicly available CREMA-D dataset, while the unlabeled target data come from real-life public speaking tasks. Since fear is one of the major factors of PSA, the goal of this study is to build fear-specific representations from the labeled source data to estimate the degree of fear in the target data, and examine the extent to which the latter is associated with anxiety during the public speaking encounter. Transfer learning is performed through the domain-adversarial neural network (DANN) and Wasserstein generative adversarial network (WGAN). Results indicate that the proposed unsupervised fear- specific estimates can detect public speaking anxiety with Pearson’s correlation coefficient of 0.28 (p <0.01). When these fear- specific estimates are combined with the degree of an individual’s preparation for the public speaking task, obtained through selfreports, they yield Pearson’s correlation of 0.55 (p <0.01). These indicate the feasibility of leveraging labeled emotion-specific corpora for detecting human-related outcomes in real-life and provides a foundation for smart assistive technologies through the automated real-time estimation of anxiety during public speaking.","PeriodicalId":221378,"journal":{"name":"2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117007304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Wide Separate 3D Convolution for Video Super Resolution 宽分离3D卷积视频超分辨率

2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP) Pub Date : 2019-11-01 DOI: 10.1109/GlobalSIP45357.2019.8969481

Xiafei Yu, Jiying Zhao

引用次数: 1

A Novel Quantization Method for Deep Learning-Based Massive MIMO CSI Feedback 一种新的基于深度学习的海量MIMO CSI反馈量化方法

2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP) Pub Date : 2019-11-01 DOI: 10.1109/GlobalSIP45357.2019.8969557

Tong Chen, Jiajia Guo, Shi Jin, Chao-Kai Wen, Geoffrey Y. Li

引用次数: 19

ITD Modeling Based on Anthropometrics and KEMAR Coefficients Using Deep Neural Networks 基于人体测量学和KEMAR系数的深度神经网络过渡段建模

2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP) Pub Date : 2019-11-01 DOI: 10.1109/GlobalSIP45357.2019.8969348

Saif S. Alotaibi, M. Wickert

引用次数: 0

Partial Discharge Classification in Power Electronics Applications using Machine Learning 机器学习在电力电子应用中的局部放电分类

2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP) Pub Date : 2019-11-01 DOI: 10.1109/GlobalSIP45357.2019.8969553

Ebrahim Balouji, T. Hammarström, T. McKelvey

{"title":"Partial Discharge Classification in Power Electronics Applications using Machine Learning","authors":"Ebrahim Balouji, T. Hammarström, T. McKelvey","doi":"10.1109/GlobalSIP45357.2019.8969553","DOIUrl":"https://doi.org/10.1109/GlobalSIP45357.2019.8969553","url":null,"abstract":"A study of machine learning (ML) methods for classification of data from partial discharges (PDs) is described. A novel set of features are suggested and tested using an extensive set of machine learning based algorithms. The aim is to classify PDs occurring within insulation systems and power electronics devices (PED). Due to the increased use of pulse width modulation waveform (PWM) in PEDs, an increased insulation degradation has been observed due to a more intense PD exposure. This study aims to develop suitable tools to detect types of defects to facilitate diagnostics as well as to improve isolation system design. To evaluate the performance of ML based classification, several algorithms have been developed to detect and classify PDs from different kind of material defects with the aim to address the reason behind the appearance of partial discharges. Experiments with different PD source locations and volume of the defect and voltage rise time were investigated on an artificial cavity test object. Relevant signal features found important are for example the maximum magnitude, duration, the distance from polarity shift, the time distance between PDs and the absolute value of the area of the detected PD waveform. It has been observed that forming such PD features based on their time occurrence results in an accurate and generalized solution. With these features the best results were achieved with the deep learning LSTM architecture reaching a test accuracy of 98.3%. For industry applications, feature engineering is useful to reduce amount of data necessary to be analyzed by the neural network or ML algorithm.","PeriodicalId":221378,"journal":{"name":"2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116010669","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

Machine Learning-Based Roadside Vehicular Traffic Localization via Opportunistic Wireless Sensing 基于机器学习的路边车辆交通定位机会无线传感

2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP) Pub Date : 2019-11-01 DOI: 10.1109/GlobalSIP45357.2019.8969382

Kyle W. Mcclintick, M. Page, T. Wickramarathne, A. Wyglinski

{"title":"Machine Learning-Based Roadside Vehicular Traffic Localization via Opportunistic Wireless Sensing","authors":"Kyle W. Mcclintick, M. Page, T. Wickramarathne, A. Wyglinski","doi":"10.1109/GlobalSIP45357.2019.8969382","DOIUrl":"https://doi.org/10.1109/GlobalSIP45357.2019.8969382","url":null,"abstract":"Comprehensive Situational Awareness (SA) in mixed traffic environments (i.e., both autonomous and human-operated platforms) is a critical requirement in addressing some of the challenges that hinder the deployment of autonomous vehicle (AV) systems onto roadways. In this paper, a novel framework that leverages machine learning techniques for utilizing Signals of Opportunity (SoO) for robust localization of all vehicles operating along a stretch of roadway is presented. By making use of ubiquitous wireless emissions from vehicles, the presented approach performs vehicle localization without any active participation/assistance from vehicles thus making it a suitable candidate for SA in mixed traffic environments. Our simulation results show that given the road shape and number of vehicles present, observed 2D localization estimates generated by an arbitrary algorithm whose error is described by a Gaussian bivariate distribution with 10 meters covariance yields unbiased vehicle centroid estimates with less than one meter mean squared error by eight Kalman Filter (KF) iterations. A set of KFs for each vehicle are used to leverage the filtering of multiple estimates per vehicle per filter step to reduce measurement noise by averaging, while a clustering algorithm performs the dual role of forming KF set priors and classifying location estimates to their correct vehicle.","PeriodicalId":221378,"journal":{"name":"2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130794023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evaluating goal-advice appropriateness for personal financial advice 评估个人财务建议的目标建议的适当性

2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP) Pub Date : 2019-11-01 DOI: 10.1109/GlobalSIP45357.2019.8969340

S. A. Chen, Adam J. Makarucha, Nebula Alam, W. Sherchan, Simon Harris, G. Yiapanis, Christopher J. Butler

引用次数: 2

Speech Recognition Driven Assistive Framework for Remote Patient Monitoring 语音识别驱动的远程病人监测辅助框架

2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP) Pub Date : 2019-11-01 DOI: 10.1109/GlobalSIP45357.2019.8969464

Marc Jayson Baucas, P. Spachos

{"title":"Speech Recognition Driven Assistive Framework for Remote Patient Monitoring","authors":"Marc Jayson Baucas, P. Spachos","doi":"10.1109/GlobalSIP45357.2019.8969464","DOIUrl":"https://doi.org/10.1109/GlobalSIP45357.2019.8969464","url":null,"abstract":"Health care resources have started to become scarce due to their increase in demand. Hospitals have begun to run out of space, forcing them to deny admission of patients. Remote Patient Monitoring (RPM) has the potential to help citizens who suffer from chronic diseases and provide environments were easy to access healthcare is available. RPM allows people to receive the same amount of care without having to difficulties to find a spot at a hospital ward. However, some roadblocks end up preventing RPM from being implemented by more healthcare providers. Data integrity, user privacy, and high power consumption are some of these concerns. With data transmission and transaction, privacy and confidentiality have always been an issue. High power consumption is a concern due to RPM’s demand for continuous data collection. This paper proposes a framework that reinforces the RPM system to address these concerns. The design not only allows better data filtering for privacy but also a more responsive system with the use of controlled surveillance and speech recognition. Overall, this framework provides an opportunity for RPMs to be a viable implementation for healthcare providers.","PeriodicalId":221378,"journal":{"name":"2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128183508","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3