ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)最新文献_第4页

Fire Detection in H.264 Compressed Video H.264压缩视频中的火灾检测

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2019-05-12 DOI: 10.1109/ICASSP.2019.8683666

Murat Muhammet Savci, Yasin Yildirim, Gorkem Saygili, B. U. Töreyin

引用次数: 7

Passive Detection and Discrimination of Body Movements in the sub-THz Band: A Case Study 亚太赫兹波段身体运动的被动检测和识别:一个案例研究

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2019-05-12 DOI: 10.1109/ICASSP.2019.8682165

S. Kianoush, S. Savazzi, V. Rampa

{"title":"Passive Detection and Discrimination of Body Movements in the sub-THz Band: A Case Study","authors":"S. Kianoush, S. Savazzi, V. Rampa","doi":"10.1109/ICASSP.2019.8682165","DOIUrl":"https://doi.org/10.1109/ICASSP.2019.8682165","url":null,"abstract":"Passive radio sensing technique is a well established research topic where radio-frequency (RF) devices are used as real-time virtual probes that are able to detect the presence and the movement(s) of one or more (non instrumented) subjects. However, radio sensing methods usually employ frequencies in the unlicensed 2.4−5.0 GHz bands where multipath effects strongly limit their accuracy, thus reducing their wide acceptance. On the contrary, sub-terahertz (sub-THz) radiation, due to its very short wavelength and reduced multipath effects, is well suited for high-resolution body occupancy detection and vision applications. In this paper, for the first time, we adopt radio devices emitting in the 100 GHz band to process an image of the environment for body motion discrimination inside a workspace area. Movement detection is based on the real-time analysis of body-induced signatures that are estimated from sub-THz measurements and then processed by specific neural network-based classifiers. Experimental trials are employed to validate the proposed methods and compare their performances with application to industrial safety monitoring.","PeriodicalId":13203,"journal":{"name":"ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"5 1","pages":"1597-1601"},"PeriodicalIF":0.0,"publicationDate":"2019-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84182799","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

Learning the Spiral Sharing Network with Minimum Salient Region Regression for Saliency Detection 学习最小显著区回归的螺旋共享网络显著性检测

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2019-05-12 DOI: 10.1109/ICASSP.2019.8682531

Zukai Chen, Xin Tan, Hengliang Zhu, Shouhong Ding, Lizhuang Ma, Haichuan Song

引用次数: 0

Autoencoding HRTFS for DNN Based HRTF Personalization Using Anthropometric Features 自动编码HRTFS基于DNN的HRTF个性化使用人体特征

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2019-05-12 DOI: 10.1109/ICASSP.2019.8683814

Tzu-Yu Chen, Tzu-Hsuan Kuo, T. Chi

引用次数: 21

Multicast Beamforming Using Semidefinite Relaxation and Bounded Perturbation Resilience 基于半定松弛和有界扰动弹性的组播波束形成

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2019-05-12 DOI: 10.1109/ICASSP.2019.8682325

Jochen Fink, R. Cavalcante, S. Stańczak

引用次数: 2

1-D Convolutional Neural Networks for Signal Processing Applications 一维卷积神经网络在信号处理中的应用

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2019-05-12 DOI: 10.1109/ICASSP.2019.8682194

S. Kiranyaz, T. Ince, Osama Abdeljaber, Onur Avcı, M. Gabbouj

{"title":"1-D Convolutional Neural Networks for Signal Processing Applications","authors":"S. Kiranyaz, T. Ince, Osama Abdeljaber, Onur Avcı, M. Gabbouj","doi":"10.1109/ICASSP.2019.8682194","DOIUrl":"https://doi.org/10.1109/ICASSP.2019.8682194","url":null,"abstract":"1D Convolutional Neural Networks (CNNs) have recently become the state-of-the-art technique for crucial signal processing applications such as patient-specific ECG classification, structural health monitoring, anomaly detection in power electronics circuitry and motor-fault detection. This is an expected outcome as there are numerous advantages of using an adaptive and compact 1D CNN instead of a conventional (2D) deep counterparts. First of all, compact 1D CNNs can be efficiently trained with a limited dataset of 1D signals while the 2D deep CNNs, besides requiring 1D to 2D data transformation, usually need datasets with massive size, e.g., in the \"Big Data\" scale in order to prevent the well-known \"overfitting\" problem. 1D CNNs can directly be applied to the raw signal (e.g., current, voltage, vibration, etc.) without requiring any pre- or post-processing such as feature extraction, selection, dimension reduction, denoising, etc. Furthermore, due to the simple and compact configuration of such adaptive 1D CNNs that perform only linear 1D convolutions (scalar multiplications and additions), a real-time and low-cost hardware implementation is feasible. This paper reviews the major signal processing applications of compact 1D CNNs with a brief theoretical background. We will present their state-of-the-art performances and conclude with focusing on some major properties. Keywords – 1-D CNNs, Biomedical Signal Processing, SHM","PeriodicalId":13203,"journal":{"name":"ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"8 1","pages":"8360-8364"},"PeriodicalIF":0.0,"publicationDate":"2019-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87978515","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: 145

An Empirical Study of Speech Processing in the Brain by Analyzing the Temporal Syllable Structure in Speech-input Induced EEG 通过分析语音输入诱发脑电图的时态音节结构对大脑语音处理的实证研究

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2019-05-12 DOI: 10.1109/ICASSP.2019.8683572

Rini A. Sharon, Shrikanth S. Narayanan, M. Sur, H. Murthy

{"title":"An Empirical Study of Speech Processing in the Brain by Analyzing the Temporal Syllable Structure in Speech-input Induced EEG","authors":"Rini A. Sharon, Shrikanth S. Narayanan, M. Sur, H. Murthy","doi":"10.1109/ICASSP.2019.8683572","DOIUrl":"https://doi.org/10.1109/ICASSP.2019.8683572","url":null,"abstract":"Clinical applicability of electroencephalography (EEG) is well established, however the use of EEG as a choice for constructing brain computer interfaces to develop communication platforms is relatively recent. To provide more natural means of communication, there is an increasing focus on bringing together speech and EEG signal processing. Quantifying the way our brain processes speech is one way of approaching the problem of speech recognition using brain waves. This paper analyses the feasibility of recognizing syllable level units by studying the temporal structure of speech reflected in the EEG signals. The slowly varying component of the delta band EEG(0.3-3Hz) is present in all other EEG frequency bands. Analysis shows that removing the delta trend in EEG signals results in signals that reveals syllable like structure. Using a 25 syllable framework, classification of EEG data obtained from 13 subjects yields promising results, underscoring the potential of revealing speech related temporal structure in EEG.","PeriodicalId":13203,"journal":{"name":"ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"33 1","pages":"4090-4094"},"PeriodicalIF":0.0,"publicationDate":"2019-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86300490","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: 11

CRF-based Single-stage Acoustic Modeling with CTC Topology 基于crf的CTC拓扑单级声学建模

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2019-05-12 DOI: 10.1109/ICASSP.2019.8682256

Hongyu Xiang, Zhijian Ou

引用次数: 23

Enhanced Virtual Singers Generation by Incorporating Singing Dynamics to Personalized Text-to-speech-to-singing 增强虚拟歌手的产生，结合唱歌动态个性化的文本到语音到唱歌

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2019-05-12 DOI: 10.1109/ICASSP.2019.8682968

Kantapon Kaewtip, F. Villavicencio, Fang-Yu Kuo, Mark Harvilla, I. Ouyang, P. Lanchantin

引用次数: 5

Tensor Super-resolution for Seismic Data 地震数据的张量超分辨率

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2019-05-12 DOI: 10.1109/ICASSP.2019.8683419

Songjie Liao, Xiao-Yang Liu, Feng Qian, Miao Yin, Guangmin Hu

引用次数: 3