2018 IEEE 23rd International Conference on Digital Signal Processing (DSP)最新文献_第7页

Automatic Verbal Analysis of Interviews with Schizophrenic Patients 精神分裂症患者访谈的自动言语分析

2018 IEEE 23rd International Conference on Digital Signal Processing (DSP) Pub Date : 2018-11-01 DOI: 10.1109/ICDSP.2018.8631830

Shihao Xu, Zixu Yang, Debsubhra Chakraborty, Yasir Tahir, Tomasz Maszczyk, Y. H. V. Chua, J. Dauwels, D. Thalmann, N. Magnenat-Thalmann, Bhing-Leet Tan, J. Lee

{"title":"Automatic Verbal Analysis of Interviews with Schizophrenic Patients","authors":"Shihao Xu, Zixu Yang, Debsubhra Chakraborty, Yasir Tahir, Tomasz Maszczyk, Y. H. V. Chua, J. Dauwels, D. Thalmann, N. Magnenat-Thalmann, Bhing-Leet Tan, J. Lee","doi":"10.1109/ICDSP.2018.8631830","DOIUrl":"https://doi.org/10.1109/ICDSP.2018.8631830","url":null,"abstract":"Schizophrenia is a long-term mental disease associated with language impairments that affect about one percent of the population. Traditional assessment of schizophrenic patients is conducted by trained professionals, which requires tremendous resources of time and effort. This study is part of a larger research objective committed to creating automated platforms to aid clinical diagnosis and understanding of schizophrenia. We have analyzed non-verbal cues and movement signals in our previous work. In this study, we explore the feasibility of using automatic transcriptions of interviews to classify patients and predict the observability of negative symptoms in schizophrenic patients. Interview recordings of 50 schizophrenia patients and 25 age-matched healthy controls were automatically transcribed by a speech recognition toolkit. After which, Natural Language Processing techniques were applied to automatically extract the lexical features and document vectors of transcriptions. Using these features, we applied ensemble machine learning algorithm (by leave-one-out cross-validation) to predict the Negative Symptom Assessment subject ratings of schizophrenic patients, and to classify patients from controls, achieving a maximum accuracy of 78.7%. These results indicate that schizophrenic patients exhibit significant differences in lexical usage compared with healthy controls, and the possibility of using these lexical features in the understanding and diagnosis of schizophrenia.","PeriodicalId":218806,"journal":{"name":"2018 IEEE 23rd International Conference on Digital Signal Processing (DSP)","volume":" 111","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132188031","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: 8

A Design of Variable Digital Filters Based on FRM Technique and Frequency Warping 基于FRM技术和频率翘曲的可变数字滤波器设计

2018 IEEE 23rd International Conference on Digital Signal Processing (DSP) Pub Date : 2018-11-01 DOI: 10.1109/ICDSP.2018.8631608

Yang Chen, Tong Ma, Ying Wei

引用次数: 1

Multiple Classifiers Global Dynamic Fusion Location System based on WiFi and Geomagnetism 基于WiFi和地磁的多分类器全球动态融合定位系统

2018 IEEE 23rd International Conference on Digital Signal Processing (DSP) Pub Date : 2018-11-01 DOI: 10.1109/ICDSP.2018.8631691

Feng-yan Xu, Linfu Duan, Xiansheng Guo, Lin Li, F. Hu

{"title":"Multiple Classifiers Global Dynamic Fusion Location System based on WiFi and Geomagnetism","authors":"Feng-yan Xu, Linfu Duan, Xiansheng Guo, Lin Li, F. Hu","doi":"10.1109/ICDSP.2018.8631691","DOIUrl":"https://doi.org/10.1109/ICDSP.2018.8631691","url":null,"abstract":"The existing WiFi and geomagnetism based positioning methods using single classifier show low accuracy because they are sensitive to changing environments. In this paper, we propose a global dynamic fusion location algorithm for multiple classifiers based on WiFi and geomagnetic fingerprints. In the offline phase, we first divide a positioning environment into some grid points and construct RSS and geomagnetic fingerprints for each grid point. Then, we train multiple classifiers by using the constructed fingerprints. Second, we derive a global dynamic fusion weight training method for each grid point through the global supervised optimization learning. In the online phase, given an RSS testing sample, we select the matching weights for fusion by using K-nearest neighbor (KNN). Our proposed multiple classifiers global dynamic fusion algorithm can make full use of the intrinsic complementarity of multiple classifiers, thus effectively improving the positioning accuracy of RSS and geomagnetic fingerprints. Experimental results show that the proposed algorithm outperforms some existing methods in complex indoor environments.","PeriodicalId":218806,"journal":{"name":"2018 IEEE 23rd International Conference on Digital Signal Processing (DSP)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115114361","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: 5

Improved Nonparallel Hyperplanes Support Vector Machines for Multi-class Classification 多类分类的改进非并行超平面支持向量机

2018 IEEE 23rd International Conference on Digital Signal Processing (DSP) Pub Date : 2018-11-01 DOI: 10.1109/ICDSP.2018.8631672

F. Bai, Ruijie Liu

引用次数: 2

Effect of Steering Vector Estimation on MVDR Beamformer for Noisy Speech Recognition 转向矢量估计对MVDR波束形成器噪声语音识别的影响

2018 IEEE 23rd International Conference on Digital Signal Processing (DSP) Pub Date : 2018-11-01 DOI: 10.1109/ICDSP.2018.8631808

Xingwei Sun, Ziteng Wang, Risheng Xia, Junfeng Li, Yonghong Yan

{"title":"Effect of Steering Vector Estimation on MVDR Beamformer for Noisy Speech Recognition","authors":"Xingwei Sun, Ziteng Wang, Risheng Xia, Junfeng Li, Yonghong Yan","doi":"10.1109/ICDSP.2018.8631808","DOIUrl":"https://doi.org/10.1109/ICDSP.2018.8631808","url":null,"abstract":"The minimum variance distortionless response (MV-DR) beamformer is a widely used beamforming technique that extracts sound components coming from a direction specified by a steering vector. In this paper, we present four different steering vector estimation methods and analyze their influence on the MVDR beamformer in speech recognition. The first one is based on the direction of arrival under the plane wave propagation assumption with the prior knowledge of microphone array geometry. The other three methods are based on the decomposition of the observed speech covariance matrix, including the covariance subtraction based method, the eigenvalue decomposition based method, and the generalized eigenvalue decomposition (GEVD) based method. We theoretically prove that the three decomposition based methods are equivalent under the narrowband approximation or after the rank -1 speech covariance matrix approximation. The speech recognition experiments conducted on the CHiME-3 dataset shows that the MVDR beamformer using GEVD-based steering vector estimation achieves the best performance, and word error rates can be further reduced with the rank -1 approximation.","PeriodicalId":218806,"journal":{"name":"2018 IEEE 23rd International Conference on Digital Signal Processing (DSP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121222727","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: 8

Average Case Analysis of Compressive Multichannel Frequency Estimation Using Atomic Norm Minimization 基于原子范数最小化的压缩多信道频率估计的平均案例分析

2018 IEEE 23rd International Conference on Digital Signal Processing (DSP) Pub Date : 2018-11-01 DOI: 10.1109/ICDSP.2018.8631803

Zai Yang, Yonina C. Eldar, Lihua Xie

引用次数: 1

On Interference Alignment Based NOMA for Downlink Multicell Transmissions 基于NOMA的下行多小区传输干扰对准研究

2018 IEEE 23rd International Conference on Digital Signal Processing (DSP) Pub Date : 2018-11-01 DOI: 10.1109/ICDSP.2018.8631573

Micael Bernhardt, J. Cousseau

引用次数: 0

Hearing loss identification via wavelet entropy and combination of Tabu search and particle swarm optimization 基于小波熵和禁忌搜索与粒子群优化相结合的听力损失识别

2018 IEEE 23rd International Conference on Digital Signal Processing (DSP) Pub Date : 2018-11-01 DOI: 10.1109/ICDSP.2018.8631839

Chaosheng Tang, Elizabeth Lee

引用次数: 13

Dummy Signal Precoding for PAPR Reduction in MIMO Communication System MIMO通信系统中减小PAPR的虚信号预编码

2018 IEEE 23rd International Conference on Digital Signal Processing (DSP) Pub Date : 2018-11-01 DOI: 10.1109/ICDSP.2018.8631848

Kai Kang, Zhou Fang, Haifeng Wang, H. Qian, Yang Yang

引用次数: 1

Automatic Diagnosis of Thyroid Ultrasound Image Based on FCN-AlexNet and Transfer Learning 基于FCN-AlexNet和迁移学习的甲状腺超声图像自动诊断

2018 IEEE 23rd International Conference on Digital Signal Processing (DSP) Pub Date : 2018-11-01 DOI: 10.1109/ICDSP.2018.8631796

Jianguo Sun, Tianxu Sun, Ye Yuan, Xingjian Zhang, Yiqi Shi, Yun Lin

引用次数: 12