2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP)最新文献_第8页

Defeating reverberation: Advanced dereverberation and recognition techniques for hands-free speech recognition 击败混响:先进的去混响和识别技术，免提语音识别

2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP) Pub Date : 2014-12-01 DOI: 10.1109/GlobalSIP.2014.7032172

Marc Delcroix, Takuya Yoshioka, A. Ogawa, Yotaro Kubo, M. Fujimoto, N. Ito, K. Kinoshita, Miquel Espi, S. Araki, Takaaki Hori, T. Nakatani

{"title":"Defeating reverberation: Advanced dereverberation and recognition techniques for hands-free speech recognition","authors":"Marc Delcroix, Takuya Yoshioka, A. Ogawa, Yotaro Kubo, M. Fujimoto, N. Ito, K. Kinoshita, Miquel Espi, S. Araki, Takaaki Hori, T. Nakatani","doi":"10.1109/GlobalSIP.2014.7032172","DOIUrl":"https://doi.org/10.1109/GlobalSIP.2014.7032172","url":null,"abstract":"Automatic speech recognition is being used successfully in more and more products. However, current recognition systems usually require the use of close-talking microphones. This constraint limits the deployment of speech recognition for new applications. In hands-free situations, noise and reverberation cause a severe degradation of the recognition performance. The problem of noise robustness has attracted a great deal of attention and practical solutions have been proposed and evaluated with common benchmarks. In contrast, reverberation has long been considered an unsolvable problem. Recently, significant progress has been made in the field of reverberant speech recognition and this progress has been evaluated with the REVERB challenge 2014. In this paper, we describe the reverberant speech recognition system we proposed for the REVERB challenge that exhibited high recognition performance even under severe reverberation conditions. We compare our system with other proposed approaches to suggest potential future research directions in the field.","PeriodicalId":362306,"journal":{"name":"2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"195 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114748657","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

Learning multidimensional Fourier series with tensor trains 用张量训练学习多维傅立叶级数

2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP) Pub Date : 2014-12-01 DOI: 10.1109/GlobalSIP.2014.7032146

S. Wahls, V. Koivunen, H. Poor, M. Verhaegen

{"title":"Learning multidimensional Fourier series with tensor trains","authors":"S. Wahls, V. Koivunen, H. Poor, M. Verhaegen","doi":"10.1109/GlobalSIP.2014.7032146","DOIUrl":"https://doi.org/10.1109/GlobalSIP.2014.7032146","url":null,"abstract":"How to learn a function from observations of inputs and noisy outputs is a fundamental problem in machine learning. Often, an approximation of the desired function is found by minimizing a risk functional over some function space. The space of candidate functions should contain good approximations of the true function, but it should also be such that the minimization of the risk functional is computationally feasible. In this paper, finite multidimensional Fourier series are used as candidate functions. Their impressive approximative capabilities are illustrated by showing that Gaussian-kernel estimators can be approximated arbitrarily well over any compact set of bandwidths with a fixed number of Fourier coefficients. However, the solution of the associated risk minimization problem is computationally feasible only if the dimension d of the inputs is small because the number of required Fourier coefficients grows exponentially with d. This problem is addressed by using the tensor train format to model the tensor of Fourier coefficients under a low-rank constraint. An algorithm for least-squares regression is derived and the potential of this approach is illustrated in numerical experiments. The computational complexity of the algorithm grows only linearly both with the number of observations N and the input dimension d, making it feasible also for large-scale problems.","PeriodicalId":362306,"journal":{"name":"2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128506852","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

On adaptive pixel random selection for compressive sensing 压缩感知中的自适应像素随机选择

2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP) Pub Date : 2014-12-01 DOI: 10.1109/GlobalSIP.2014.7032211

W. Guicquero, P. Vandergheynst, T. Laforest, A. Dupret

引用次数: 3

Joint power allocation and subcarrier selection for energy efficiency maximization in OFDM systems under a holistic power model 在整体功率模型下OFDM系统的联合功率分配和子载波选择以实现能效最大化

2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP) Pub Date : 2014-12-01 DOI: 10.1109/GlobalSIP.2014.7032102

Liwei Yan, B. Bai, Wei Chen

{"title":"Joint power allocation and subcarrier selection for energy efficiency maximization in OFDM systems under a holistic power model","authors":"Liwei Yan, B. Bai, Wei Chen","doi":"10.1109/GlobalSIP.2014.7032102","DOIUrl":"https://doi.org/10.1109/GlobalSIP.2014.7032102","url":null,"abstract":"Energy efficiency (EE) maximization for OFDM transceiver has received much attention in next generation wireless communication systems. Recently, there have been a lot of works on EE maximization by balancing the transmission rate and the holistic power consumption. However, how to maximize EE in OFDM systems by joint power allocation and subcarrier selection under holistic power models has not been extensively investigated yet. In contrast to spectrum efficiency (SE) oriented power allocation and subcarrier selection, which is by a simple waterfilling policy, the EE oriented power allocation and subcarrier selection is a mixed integer programming, which is not trivial to solve. This is simply because an extra circuit power has to be paid as the cost of adding an subcarrier. Fortunately, by delving into the mixed integer programming, we found in this paper that the EE oriented power allocation also has form of waterfilling. Based on these results and approximate methods, explicit decision criterions are proposed for subcarrier selection, which provides insight into how the transmit power and the number of subcarriers balance with the channel states and the circuit power in EE oriented systems. Numerical results show that the proposed strategies achieve near-optimal performance of EE with low complexity.","PeriodicalId":362306,"journal":{"name":"2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126987008","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

Spatial rainfall mapping from path-averaged rainfall measurements exploiting sparsity 利用稀疏性的路径平均降雨量测量的空间降雨制图

2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP) Pub Date : 2014-12-01 DOI: 10.1109/GlobalSIP.2014.7032131

Venkat Roy, S. Gishkori, G. Leus

引用次数: 5

A network-based analysis of ischemic stroke using parallel microRNA-mRNA expression profiles 利用平行microRNA-mRNA表达谱对缺血性卒中进行基于网络的分析

2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP) Pub Date : 2014-12-01 DOI: 10.1109/GlobalSIP.2014.7032361

Yingying Wang, Yunpeng Cai

引用次数: 1

Achieving worst case robustness in energy efficient multiuser multicell cooperation systems 实现节能多用户多小区合作系统的最坏情况鲁棒性

2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP) Pub Date : 2014-12-01 DOI: 10.1109/GlobalSIP.2014.7032082

Yuke Cui, Wei Xu, Hua Zhang, X. You

引用次数: 0

Dictionary learning based nonlinear classifier training from distributed data 基于字典学习的分布式非线性分类器训练

2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP) Pub Date : 2014-12-01 DOI: 10.1109/GlobalSIP.2014.7032221

Z. Shakeri, Haroon Raja, W. Bajwa

引用次数: 6

Uncoded transmission of correlated Gaussian sources over broadcast channels with feedback 相关高斯源在带反馈的广播信道上的无编码传输

2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP) Pub Date : 2014-12-01 DOI: 10.1109/GlobalSIP.2014.7032249

Yonathan Murin, Y. Kaspi, R. Dabora, Deniz Gündüz

引用次数: 3

Diversified parameter estimation in complex networks 复杂网络中的多元参数估计

2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP) Pub Date : 2014-12-01 DOI: 10.1109/GlobalSIP.2014.7032251

A. Tajer

引用次数: 1