ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)最新文献_第7页

Compressed Sensing Based Channel Estimation and Open-loop Training Design for Hybrid Analog-digital Massive MIMO Systems 基于压缩感知的混合模数大规模MIMO系统信道估计与开环训练设计

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2020-05-01 DOI: 10.1109/ICASSP40776.2020.9054443

Khaled Ardah, Bruno Sokal, A. D. Almeida, M. Haardt

{"title":"Compressed Sensing Based Channel Estimation and Open-loop Training Design for Hybrid Analog-digital Massive MIMO Systems","authors":"Khaled Ardah, Bruno Sokal, A. D. Almeida, M. Haardt","doi":"10.1109/ICASSP40776.2020.9054443","DOIUrl":"https://doi.org/10.1109/ICASSP40776.2020.9054443","url":null,"abstract":"Channel estimation in hybrid analog-digital massive MIMO systems is a challenging problem due to the high channel dimension, low signal-to-noise ratio before beamforming, and reduced number of radio-frequency chains. Compressed sensing based algorithms have been adopted to address these challenges by leveraging the sparse nature of millimeter-wave MIMO channels. In compressed sensing-based methods, the training vectors should be designed carefully to guarantee recoverability. Although using random vectors has an overwhelming recoverability guarantee, it has been recently shown that an optimized update, which could be obtained so that the mutual coherence of the resulting sensing matrix is minimized, can improve the recoverability guarantee. In this paper, we propose an openloop hybrid analog-digital beam-training framework, where a given sensing matrix is decomposed into analog and digital beamformers. The given sensing matrix can be designed efficiently offline to reduce computational complexity. Simulation results show that the proposed training method achieves a lower mutual coherence and an improved channel estimation performance than the other benchmark methods.","PeriodicalId":13127,"journal":{"name":"ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"1 1","pages":"4597-4601"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76943885","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: 6

Joint Training of Deep Neural Networks for Multi-Channel Dereverberation and Speech Source Separation 深度神经网络多通道去噪与语音源分离的联合训练

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2020-05-01 DOI: 10.1109/ICASSP40776.2020.9053791

M. Togami

{"title":"Joint Training of Deep Neural Networks for Multi-Channel Dereverberation and Speech Source Separation","authors":"M. Togami","doi":"10.1109/ICASSP40776.2020.9053791","DOIUrl":"https://doi.org/10.1109/ICASSP40776.2020.9053791","url":null,"abstract":"In this paper, we propose a joint training of two deep neural networks (DNNs) for dereverberation and speech source separation. The proposed method connects the first DNN, the dereverberation part, the second DNN, and the speech source separation part in a cascade manner. The proposed method does not train each DNN separately. Instead, an integrated loss function which evaluates an output signal after dereverberation and speech source separation is adopted. The proposed method estimates the output signal as a probabilistic variable. Recently, in the speech source separation context, we proposed a loss function which evaluates the estimated posterior probability density function (PDF) of the output signal. In this paper, we extend this loss function into a loss function which evaluates not only speech source separation performance but also speech derevereberation performance. Since the output signal of the dereverberation part is converted into the input feature of the second DNN, gradient of the loss function is back-propagated into the first DNN through the input feature of the second DNN. Experimental results show that the proposed joint training of two DNNs is effective. It is also shown that the posterior PDF based loss function is effective in the joint training context.","PeriodicalId":13127,"journal":{"name":"ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"10 1","pages":"3032-3036"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81344185","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

Deblurring And Super-Resolution Using Deep Gated Fusion Attention Networks For Face Images 基于深度门控融合注意网络的人脸图像去模糊和超分辨率

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2020-05-01 DOI: 10.1109/ICASSP40776.2020.9053784

Chao Yang, Long-Wen Chang

引用次数: 5

Adversarial Multi-Task Learning for Speaker Normalization in Replay Detection 重播检测中说话人归一化的对抗性多任务学习

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2020-05-01 DOI: 10.1109/ICASSP40776.2020.9054322

Gajan Suthokumar, V. Sethu, Kaavya Sriskandaraja, E. Ambikairajah

引用次数: 6

A Differential Approach for Rain Field Tomographic Reconstruction Using Microwave Signals from Leo Satellites 利用低轨道卫星微波信号进行雨场层析成像的差分方法

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2020-05-01 DOI: 10.1109/ICASSP40776.2020.9054284

Xi Shen, D. Huang, C. Vincent, Wenxiao Wang, R. Togneri

引用次数: 3

Stochastic Graph Neural Networks 随机图神经网络

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2020-05-01 DOI: 10.1109/ICASSP40776.2020.9054424

Zhan Gao, E. Isufi, Alejandro Ribeiro

{"title":"Stochastic Graph Neural Networks","authors":"Zhan Gao, E. Isufi, Alejandro Ribeiro","doi":"10.1109/ICASSP40776.2020.9054424","DOIUrl":"https://doi.org/10.1109/ICASSP40776.2020.9054424","url":null,"abstract":"Graph neural networks (GNNs) model nonlinear representations in graph data with applications in distributed agent coordination, control, and planning among others. However, current GNN implementations assume ideal distributed scenarios and ignore link fluctuations that occur due to environment or human factors. In these situations, the GNN fails to address its distributed task if the topological randomness is not considered accordingly. To overcome this issue, we put forth the stochastic graph neural network (SGNN) model: a GNN where the distributed graph convolutional operator is modified to account for the network changes. Since stochasticity brings in a new paradigm, we develop a novel learning process for the SGNN and introduce the stochastic gradient descent (SGD) algorithm to estimate the parameters. We prove through the SGD that the SGNN learning process converges to a stationary point under mild Lipschitz assumptions. Numerical simulations corroborate the proposed theory and show an improved performance of the SGNN compared with the conventional GNN when operating over random time varying graphs.","PeriodicalId":13127,"journal":{"name":"ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"2007 1","pages":"9080-9084"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82504172","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: 25

Playing Technique Recognition by Joint Time–Frequency Scattering 基于联合时频散射的演奏技术识别

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2020-05-01 DOI: 10.1109/ICASSP40776.2020.9053474

Changhong Wang, V. Lostanlen, Emmanouil Benetos, E. Chew

引用次数: 12

An Efficient Augmented Lagrangian-Based Method for Linear Equality-Constrained Lasso 线性等式约束Lasso的一种有效增广拉格朗日方法

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2020-05-01 DOI: 10.1109/ICASSP40776.2020.9053722

Zengde Deng, Man-Chung Yue, A. M. So

引用次数: 4

Volume Reconstruction for Light Field Microscopy 光场显微镜的体积重建

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2020-05-01 DOI: 10.1109/ICASSP40776.2020.9053433

Herman Verinaz-Jadan, P. Song, Carmel L. Howe, Amanda J. Foust, P. Dragotti

{"title":"Volume Reconstruction for Light Field Microscopy","authors":"Herman Verinaz-Jadan, P. Song, Carmel L. Howe, Amanda J. Foust, P. Dragotti","doi":"10.1109/ICASSP40776.2020.9053433","DOIUrl":"https://doi.org/10.1109/ICASSP40776.2020.9053433","url":null,"abstract":"Light Field Microscopy (LFM) is a 3D imaging technique that captures volumetric information in a single snapshot. It is appealing in microscopy because of its simple implementation and the peculiarity that it is much faster than methods involving scanning. However, volume reconstruction for LFM suffers from low lateral resolution, high computational cost, and reconstruction artifacts near the native object plane. In this work, we make two contributions. First, we propose a simplification of the forward model based on a novel discretization approach that allows us to accelerate the computation without drastically increasing memory consumption. Second, we experimentally show that by including regularization priors and an appropriate initialization strategy, it is possible to remove the artifacts near the native object plane. The algorithm we use for this is ADMM. Finally, the combination of the two techniques leads to a method that outperforms classic volume reconstruction approaches (variants of Richardson-Lucy) in terms of average computational time and image quality (PSNR).","PeriodicalId":13127,"journal":{"name":"ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"40 1","pages":"1459-1463"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78993354","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

Supervised Deep Hashing for Efficient Audio Event Retrieval 有效音频事件检索的监督深度哈希

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2020-05-01 DOI: 10.1109/ICASSP40776.2020.9053766

Arindam Jati, Dimitra Emmanouilidou

引用次数: 2