ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)最新文献_第3页

Incremental Semi-Supervised Learning for Multi-Genre Speech Recognition 多类型语音识别的增量半监督学习

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2020-05-01 DOI: 10.1109/ICASSP40776.2020.9054309

B. K. Khonglah, S. Madikeri, S. Dey, H. Bourlard, P. Motlícek, J. Billa

{"title":"Incremental Semi-Supervised Learning for Multi-Genre Speech Recognition","authors":"B. K. Khonglah, S. Madikeri, S. Dey, H. Bourlard, P. Motlícek, J. Billa","doi":"10.1109/ICASSP40776.2020.9054309","DOIUrl":"https://doi.org/10.1109/ICASSP40776.2020.9054309","url":null,"abstract":"In this work, we explore a data scheduling strategy for semi-supervised learning (SSL) for acoustic modeling in automatic speech recognition. The conventional approach uses a seed model trained with supervised data to automatically recognize the entire set of unlabeled (auxiliary) data to generate new labels for subsequent acoustic model training. In this paper, we propose an approach in which the unlabelled set is divided into multiple equal-sized subsets. These subsets are processed in an incremental fashion: for each iteration a new subset is added to the data used for SSL, starting from only one subset in the first iteration. The acoustic model from the previous iteration becomes the seed model for the next one. This scheduling strategy is compared to the approach employing all unlabeled data in one-shot for training. Experiments using lattice-free maximum mutual information based acoustic model training on Fisher English gives 80% word error recovery rate. On the multi-genre evaluation sets on Lithuanian and Bulgarian relative improvements of up to 17.2% in word error rate are observed.","PeriodicalId":13127,"journal":{"name":"ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"332 1","pages":"7419-7423"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76584036","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: 17

Automatic Epileptic Seizure Onset-Offset Detection Based On CNN in Scalp EEG 基于CNN的头皮脑电图癫痫发作发作偏移自动检测

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2020-05-01 DOI: 10.1109/ICASSP40776.2020.9053143

P. Boonyakitanont, Apiwat Lek-uthai, J. Songsiri

引用次数: 12

Signal Sensing and Reconstruction Paradigms for a Novel Multi-Source Static Computed Tomography System 一种新型多源静态计算机断层扫描系统的信号感知与重构范式

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2020-05-01 DOI: 10.1109/ICASSP40776.2020.9054146

Alankar Kowtal, A. Cramer, Dufan Wu, Kai Yang, Wolfgang Krull, Ioannis Gkioulekas, Rajiv Gupta

引用次数: 2

Wideband Channel Tracking for Millimeter Wave Massive Mimo Systems with Hybrid Beamforming Reception 混合波束成形接收毫米波海量Mimo系统的宽带信道跟踪

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2020-05-01 DOI: 10.1109/ICASSP40776.2020.9053440

G. Alexandropoulos, Evangelos Vlachos, J. Thompson

{"title":"Wideband Channel Tracking for Millimeter Wave Massive Mimo Systems with Hybrid Beamforming Reception","authors":"G. Alexandropoulos, Evangelos Vlachos, J. Thompson","doi":"10.1109/ICASSP40776.2020.9053440","DOIUrl":"https://doi.org/10.1109/ICASSP40776.2020.9053440","url":null,"abstract":"Millimeter Wave (mmWave) massive Multiple Input Multiple Output (MIMO) channel tracking is a challenging task with Hybrid analog and digital BeamForming (HBF) reception architectures. The wireless channel can only be spatially sampled with directive analog beams, which results in lengthy training periods when beam codebooks are large. In this paper, we capitalize on a recently proposed HBF architecture enabling mmWave massive MIMO channel estimation with short beam training overhead, and present a matrix-completion-based channel tracking technique for time correlated HBF receivers. The considered channel tracking problem is formulated as a constrained multi-objective optimization problem incorporating the low rank and group-sparse properties of the mmWave channel as well as a popular model for its time correlation. We present an efficient algorithm for this estimation problem that is based on the alternating direction method of multipliers. Comparisons of the proposed approach over representative state-of-the-art techniques showcase the relation between the channel time correlation coefficient and the amount of beam training needed for acceptable channel estimation performance.","PeriodicalId":13127,"journal":{"name":"ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"16 1","pages":"8698-8702"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77710074","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

Fixed-Point Optimization of Transformer Neural Network 变压器神经网络的不动点优化

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2020-05-01 DOI: 10.1109/ICASSP40776.2020.9054724

Yoonho Boo, Wonyong Sung

引用次数: 12

Fully-Neural Approach to Heavy Vehicle Detection on Bridges Using a Single Strain Sensor 基于单应变传感器的桥梁重型车辆检测全神经网络方法

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2020-05-01 DOI: 10.1109/ICASSP40776.2020.9053137

T. Kawakatsu, K. Aihara, A. Takasu, J. Adachi

{"title":"Fully-Neural Approach to Heavy Vehicle Detection on Bridges Using a Single Strain Sensor","authors":"T. Kawakatsu, K. Aihara, A. Takasu, J. Adachi","doi":"10.1109/ICASSP40776.2020.9053137","DOIUrl":"https://doi.org/10.1109/ICASSP40776.2020.9053137","url":null,"abstract":"Bridge weigh-in-motion (BWIM) is a technique for detecting heavy vehicles that may cause serious damage to real bridges. BWIM is realized by analyzing the strain signals observed at places on the bridge in terms of bridge-component responses to the axle loads. In current practice, a BWIM system requires multiple strain sensors to collect vehicle properties including speed and axle positions for accurate load estimation, which may limit the system’s life-span. Furthermore, BWIM should consider a wide variety of waveforms, which may be caused by vehicle acceleration and/or the various traveling positions in lanes. In this paper, we propose a novel BWIM mechanism, which employs a deep convolutional neural network (CNN). The CNN is able to learn actual traffic conditions and achieve accurate load estimation by using only a single strain sensor. The training dataset is collected from a distant load meter, by consulting traffic surveillance cameras and identifying similar vehicles. After the system initialization, the CNN requires no additional sensors (or cameras) for axle detection, which may reduce the costs of both installation and system maintenance.","PeriodicalId":13127,"journal":{"name":"ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"45 1","pages":"3047-3051"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80076988","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

Enhanced Mixture Population Monte Carlo Via Stochastic Optimization and Markov Chain Monte Carlo Sampling 基于随机优化和马尔可夫链蒙特卡罗抽样的增强混合种群蒙特卡罗

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2020-05-01 DOI: 10.1109/ICASSP40776.2020.9053410

Yousef El-Laham, P. Djurić, M. Bugallo

引用次数: 0

A Fast Proximal Point Algorithm for Generalized Graph Laplacian Learning 广义图拉普拉斯学习的快速近点算法

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2020-05-01 DOI: 10.1109/ICASSP40776.2020.9054185

Zengde Deng, A. M. So

引用次数: 7

Enhanced Non-Local Cascading Network with Attention Mechanism for Hyperspectral Image Denoising 基于注意机制的增强非局部级联网络高光谱图像去噪

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2020-05-01 DOI: 10.1109/ICASSP40776.2020.9054630

Hanwen Ma, Ganchao Liu, Yuan Yuan

{"title":"Enhanced Non-Local Cascading Network with Attention Mechanism for Hyperspectral Image Denoising","authors":"Hanwen Ma, Ganchao Liu, Yuan Yuan","doi":"10.1109/ICASSP40776.2020.9054630","DOIUrl":"https://doi.org/10.1109/ICASSP40776.2020.9054630","url":null,"abstract":"Because of the complexity of imaging environment, hyper-spectral remote sensing images (HSIs) often suffer from different kinds of noise. Despite the success in natural image denoising, most of the existing CNN-based HSIs denoising methods still suffer from the problem of inadequate noise suppression and insufficient feature extraction. In this paper, a novel HSIs denoising algorithm based on an enhanced non-local cascading network with attention mechanism (ENCAM) is proposed, which can extract the joint spatial-spectral feature more effectively. The main contributions include: (1) the non-local structure is introduced to enlarge the receptive field to extract the spatial features more effectively; (2) multi-scale convolutions and channel attention module are applied to enhance extracted multi-scale features; (3) a cascading residual dense structure is used to extract different frequency features. Both of the theoretical analysis and the experiments indicate that the proposed method is superior to the other state-of-the-art methods on HSIs denoising.","PeriodicalId":13127,"journal":{"name":"ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"6 1","pages":"2448-2452"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79116148","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: 12

Intra Frame Rate Control for Versatile Video Coding with Quadratic Rate-Distortion Modelling 基于二次码率失真模型的通用视频编码帧内速率控制

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2020-05-01 DOI: 10.1109/ICASSP40776.2020.9054633

Yi Chen, S. Kwong, Mingliang Zhou, Shiqi Wang, Guopu Zhu, Yi Wang

引用次数: 13