检索结果-内蒙古大学图书馆

2nd International Conference on Applications of Intelligent Systems (APPIS)

作者： Dubey, Mohit Jones, Haydn Thresher, Austin Kenyon, Garrett Oberlin Coll & Conservatory Oberlin OH 44074 USA New Mexico Inst Min & Technol Socorro NM 87801 USA New Mexico Consortium Los Alamos NM USA Los Alamos Natl Lab Los Alamos NM USA

ISBN: (纸本)9781450360852

The solution to the problem of separating multiple vocal and instrumental tracks from a single audio waveform is solved naturally by the human auditory cortex but has yet to be effectively implemented computationally. In this paper, we demonstrate a neurally-inspired approach to separating bass, drums, vocals and other instruments from sparse encodings of phase-rich Fourier and Constant-Q representations of stereo musical data. Our sparse encodings are generated from learned features that are both spectrally and temporally convolutional, similar to the hemispheric lateralization of human auditory cortex. We find that learning from neurally inspired Constant-Q representations provides better separation over Fourier spectrograms due to the presence of structure that is convolutional in log-frequency that aids in the differentiation of instruments.

关键词： convolutional sparse coding source separation constant-Q transform

来源：评论

学校读者我要写书评

暂无评论

Respiration and Heartbeat Rates Measurement Based on convolutional sparse coding

Respiration and Heartbeat Rates Measurement Based on Convolu...

引用

IEEE MTT-S International Microwave Biomedical Conference (IMBioC)

作者： Wang, Pengfei Liu, Miao Zhu, Huijie Liang, Fulai Lv, Hao Li, Zhao Wang, Jianqi Air Force Mil Med Univ Dept Mil Biomed Engn Xian 710032 Peoples R China PLA Gen Hosp Northern Theater Command Shenyang 110016 Peoples R China First Engn Sci Res Inst Gen Armaments Dept Wuxi 214035 Jiangsu Peoples R China

ISBN: (纸本)9781538673959

Accurate access to respiration rate (RR) and heartbeat rate (HR) through radar is of great importance in many applications. In this paper, a novel method based on convolutional sparse coding (CSC) is proposed for respiration and heartbeat rates measurement. To solve the problem of algorithm performance degradation caused by insufficient samples, a number of samples are generated by mixing random noise with original signal. Then radar signals are processed by CSC directly in the time domain. The method is tested by a vital sign data generated by finite differences time domain (FDTD) simulation. The results demonstrate that the proposed processing approach can accurately extract the respiration and heartbeat components with the generated data of 5 seconds.

关键词： Respiration rate heartbeat rate convolutional sparse coding FDTD

来源：评论

学校读者我要写书评

暂无评论

HIERARCHICAL SUPERPIXEL RELATION GRAPH COMBINED WITH convolutional sparse coding FOR SELF-SUPERVISED HYPERSPECTRAL IMAGE DENOISING

HIERARCHICAL SUPERPIXEL RELATION GRAPH COMBINED WITH CONVOLU...

引用

IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Jiang, Zhongshun Qian, Qipeng Qiu, Yi Qian, Yuntao Zhejiang Univ Coll Comp Sci Hangzhou Peoples R China Univ Arizona Dept Appl Math Tucson AZ USA Huzhou Univ Coll Engn Huzhou Peoples R China

ISBN: (纸本)9798350320107

Self-supervised methods have recently been widely used for hyperspectral image (HSI) denoising. As only a single noisy HSI required to be restored is used for learning, effectively exploring the spatial-spectral joint information for HSI representation becomes to be a more critical problem. In this paper, we propose a self-supervised denoiser based on a blind-spot network and a new feature extraction method that combines convolutional neural network (CNN) and graph neural network (GNN). The features from the convolutional backbone network are fed into the GNN as graph nodes. With hierarchical superpixel segmentation of HSI, the feature nodes with contextual and hierarchical relationships are feature-enhanced by the powerful feature interaction capability of GNN. Experimental results show that our method is competitive compared with state-of-the-art methods.

关键词： Hyperspectral images denoising self-supervised learning convolutional sparse coding graph neural network

来源：评论

学校读者我要写书评

暂无评论

COMPRESSED SENSING RECONSTRUCTION OF DYNAMIC CONTRAST ENHANCED MRI USING GPU-ACCELERATED convolutional sparse coding 13

COMPRESSED SENSING RECONSTRUCTION OF DYNAMIC CONTRAST ENHANC...

引用

13th IEEE International Symposium on Biomedical Imaging (ISBI)

作者： Tran Minh Quan Jeong, Won-Ki Ulsan Natl Inst Sci & Technol Sch Elect & Comp Engn Ulsan South Korea

ISBN: (纸本)9781479923496;9781479923502

In this paper, we propose a data-driven image reconstruction algorithm that specifically aims to reconstruct undersampled dynamic contrast enhanced (DCE) MRI data. The proposed method is based on the convolutional sparse coding algorithm, which leverages the Fourier convolution theorem to accelerate the process of learning a collections of filters and iteratively refines the reconstruction result using the sparse codes found during the reconstruction process. We introduce a novel energy formation based on the learning over time-varing DCE-MRI images, and propose an extension of Alternating Direction Method of Multiplier (ADMM) method to solve the constrained optimization problem efficiently using the GPU. We assess the performance of the proposed method by comparing with the state-of-the-art dictionary-based compressed sensing (CS) MRI method.

关键词： convolutional sparse coding Compressed Sensing MRI GPU

来源：评论

学校读者我要写书评

暂无评论

REGULARIZATION PARAMETER-FREE convolutional sparse coding VIA PROJECTIONS ONTO THE l₁-BALL AND THE DISCREPANCY PRINCIPLE 28

REGULARIZATION PARAMETER-FREE CONVOLUTIONAL SPARSE CODING VI...

引用

IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP)

作者： Rodriguez, Paul Pontificia Univ Catolica Peru Dept Elect Engn Lima Peru

ISBN: (纸本)9781538654774

Given a set of dictionary filters, the most widely used formulation of the convolutional sparse coding (CSC) problem is convolutional basis pursuit denoising (CBPDN), in which an image is represented as a sum over a set of convolutions of coefficient maps. When the input image is noisy, CBPDN's regularization parameter greatly influences the quality of the reconstructed image. Results for an automatic and sensible selection of this parameter are very limited for the CSC / CBPDN case. In this paper we propose a regularization parameter-free method to solve the CSC problem via its projection onto the l(1)-Ball formulation coupled with a warm-start like strategy, which, driven by the Morozov's discrepancy principle, adaptively increases/decreases its constrain at each major iteration. While the time performance of our proposed method is slower than that measured when solving CSC for a fixed regularization parameter, our computational results also show that our method's reconstruction quality is, in average, very close (within 0.16 SNR, 0.16 PSNR, 0.003 SSIM) to that obtained when the regularization parameter for CBPDN is selected to produce the best (SNR) quality result.

关键词： convolutional sparse coding Lasso Morozov's discrepancy principle

来源：评论

学校读者我要写书评

暂无评论

Informed monaural source separation of music based on convolutional sparse coding 40

Informed monaural source separation of music based on convol...

引用

40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015

作者： Jao, Ping-Keng Yang, Yi-Hsuan Wohlberg, Brendt Research Center for Information Technology Innovation Academia Sinica Taiwan Theoretical Division Los Alamos National Laboratory United States

ISBN: (纸本)9781467369978

Monaural source separation is a challenging problem that has many important applications in music information retrieval. In this paper, we focus on the score-informed variant of this problem. While non-negative matrix factorization and some other approaches have been shown effective, few existing approaches have properly taken the phase information into account. There are unnatural sound in the separation result, as the phase of each source signal is considered equivalent to the phase of the mixed signal. To remedy this, we propose to perform source separation directly in the time domain using a convolutional sparse coding (CSC) approach. Evaluation on the Bach10 dataset shows that, when the instrument, pitch and onset/offset time are informed, the source to distortion ratio of the separation result reaches 8.59 dB, which is 2.02 dB higher than a state-of-the-art system called Soundprism. © 2015 IEEE.

关键词： convolutional sparse coding dictionary learning score-informed monaural source separation

来源：评论

学校读者我要写书评

暂无评论

convolutional sparse coding CLASSIFICATION MODEL FOR IMAGE CLASSIFICATION

CONVOLUTIONAL SPARSE CODING CLASSIFICATION MODEL FOR IMAGE C...

引用

IEEE International Conference on Image Processing

作者： Boheng Chen Jie Li Biyun Ma Gang Wei School of Electronic and Information South China University of Technology

ISBN: (纸本)9781467399623

In this paper, we present a novel classification model which combines the convolutional sparse coding framework with the classification strategy. In the training phase, the proposed model trained a convolutional filter bank by all images of each class. In the test phase, the label of test image is determined by all convolutional filter banks. Compared with canonical sparse representation and dictionary learning classification algorithm, more representative information of the corresponding images could be captured by the trained filters, thus better classification performance can be obtained. Experimental results on some image benchmark databases demonstrated the effectiveness of the proposed method.

关键词： convolutional sparse coding sparse representation convolutional filter Image representation Image classification Image classification sparse representation image representation Taxonomy images Filter banks Sorting algorithm

来源：评论

学校读者我要写书评

暂无评论

WINDOW-BASED convolutional sparse coding: TOWARDS A UNIFIED FRAMEWORK 49

WINDOW-BASED CONVOLUTIONAL SPARSE CODING: TOWARDS A UNIFIED ...

引用

49th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yang, Lijian Mi, Jian-Xun Wang, Guofen Li, Weisheng Chongqing Univ Posts & Telecommun Chongqing Key Lab Image Cognit Chongqing Peoples R China

ISBN: (纸本)9798350344868;9798350344851

sparse coding (SC) and Convolution sparse coding (CSC) are two widely studied sparse methods in computer vision and signal processing. SC encodes the image patches independently, however fails to utilize the correlation among them. CSC adopts a convolution operator to connect the overlapping patches but in an inflexible manner. In this paper, a novel integrated framework for the two sparse models is proposed, wherein the local correlations among patches are controllable by manipulating a window function. Moreover, the inherent border effect of a convolution model is mitigated with a carefully designed weight function. It can be demonstrated that both SC and CSC are two distinct implementations of this framework. Consequently, our unified framework provides a balanced solution by addressing the strengths and limitations of both SC and CSC. Extensive experimental results are presented to demonstrate the superiority and effectiveness of the proposed method for image inpainting tasks.

关键词： convolutional sparse coding window function weighted averaging image inpainting

来源：评论

学校读者我要写书评

暂无评论

State-sensitive convolutional sparse coding for potential biomarker identification in brain signals

引用

Science China(Information Sciences) 2024年第5期67卷 270-286页

作者： Puli WANG Yu QI Gang PAN College of Computer Science and Technology Zhejiang University State Key Laboratory of Brain-Machine Intelligence Zhejiang University MOE Frontier Science Center for Brain Science and Brain-machine Integration Zhejiang University Affiliated Mental Health Center & Hangzhou Seventh People's Hospital Zhejiang University School of Medicine

The identification of prototypical waveforms, such as sleep spindles and epileptic spikes, is crucial for the diagnosis of neurological disorders. These prototypical waveforms are usually recurrently presented in certain brain states, serving as potential biomarkers for clinical evaluations. convolutional sparse coding(CSC) approaches have demonstrated strength in identifying recurrent patterns in time-series. However,existing CSC approaches do not explicitly explore state-specific patterns, making it difficult to identify state-related biomarkers. To address this problem, we propose state-sensitive CSC to learn state-specific prototypical waveforms. Specifically, we model signals of a certain state with specific waveforms that only appear frequently in this state and background waveforms that are independent of states. Based on this,state-sensitive CSC separates state-specific waveforms from background ones explicitly by incorporating incoherence constraints into optimizations. Experiments with epilepsy brain signals demonstrate that our approach can effectively identify prototypical waveforms in pre-ictal states, providing potential biomarkers for seizure prediction. Our approach provides a promising tool for automatic biomarker candidate identification.

关键词： convolutional sparse coding prototypical waveforms state-specific dictionary biomarker identification neural signal processing

来源：评论

学校读者我要写书评

暂无评论

Noncontact Heart Rate Measurement Based on an Improved convolutional sparse coding Method Using IR-UWB Radar

引用

IEEE ACCESS 2019年 7卷 158492-158502页

作者： Wang, Pengfei Qi, Fugui Liu, Miao Liang, Fulai Xue, Huijun Zhang, Yang Lv, Hao Wang, Jianqi Fourth Mil Med Univ Dept Biomed Engn Xian 710032 Shaanxi Peoples R China Gen Hosp Northern Theater Command PLA Shenyang 110016 Liaoning Peoples R China

Impulse radio ultrawideband radar is a critical remote sensing tool for life detection and noncontact monitoring of vital signals. In noncontact monitoring via radar, the disturbance from respiration and environmental noise is considered critical for the estimation of heart rates. However, the heartbeat signal is generally distorted by breath harmonics and fluctuations in the time domain, and the frequencies of the vital signals are closely situated;thus, it is difficult to employ an ordinary frequency filter for separation. To solve this problem, a novel method was developed to extract heartbeat information. In this study, convolutional sparse coding, which is an unsupervised machine learning algorithm, was first used to model the heartbeat signal in the time domain, given the respiration and relative artifacts. The proposed scheme was then used to decompose the time-domain signals and directly obtain the heartbeat component by exploiting the sparsity of the heartbeat signal in the time domain. Furthermore, the performance of the proposed scheme was improved using a noise-assisted method, and the process was accelerated by learning from the K-singular value decomposition strategy. Finally, by testing the vital sign signals collected from the finite differences time-domain simulation and experiments, the results obtained indicate that the proposed approach is effective for the extraction of low-amplitude heartbeat signals from the respiration signal, and that it significantly improves the accuracy of heart rate evaluation.

关键词： convolutional sparse coding heart rate finite-difference time-domain respiration rate ultrawideband radar noise-assisted method

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：