检索结果-内蒙古大学图书馆

2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： He, Yudong Woo, Baeck Hyun So, Richard H.Y. The Department of Industry Engineering & Decision Analytics The Hong Kong University of Science and Technology Hong Kong

ISBN: (纸本)9798350368741

sparse component analysis (SCA) is a popular underdetermined blind speech separation (UBSS) method. It models all sources to have an identical distribution. As speeches do not have identical distribution, SCA performs suboptimal. Some studies have improved the performance of SCA by weighting the sources through a reweighting scheme. However, they are not UBSS methods because they assume that the mixing process is known. This paper proposes a novel weighting scheme, called sparse spatial component analysis (SSCA) without the need to know the mixing process. In SSCA, weights, sources, and the parameters for modeling the mixing process are jointly optimized, making it a UBSS method. Simulation experiments show that for instantaneous mixtures, SSCA outperforms SCA and reweighted SCA, improving the source-to-distortion ratio (SDR) by 4 dB and reducing the computational time by 40%. Further, experiments using real-world recordings reveal that SSCA outperforms multichannel non-negative matrix factorization and full-rank covariance analysis (FCA) in terms of SDR. The speed of SSCA is 200% faster than FCA. © 2025 IEEE.

关键词： blind source separation sparse component analysis underdetermined linear system weighted l1 minimization

来源：评论

学校读者我要写书评

暂无评论

sparse component analysis Based on Hierarchical Hough Transform

引用

CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2017年第4期36卷 1569-1585页

作者： Jin, Yi Qin, Shaoqian Zhu, Changan Univ Sci & Technol China Dept Precis Machinery & Precis Instrumentat Hefei 230027 Anhui Peoples R China

sparse component analysis (SCA) has been extensively studied to solve undetermined blind source separation problem in various fields over the last decades. This paper proposes a SCA algorithm based on hierarchical Hough transform. The hyperplanes clustering within the mixture space are revealed as local maxima in the parameter space after Hough transform is performed. Then, the local maxima are picked up, and the mixing matrix is calculated. The grid resolution in which the parameter space is divided plays an important role on the estimation error and the computational load. Therefore, the parameter space is divided into hypercubes recursively from low to high resolution, and Hough transform is performed only on the hypercubes with votes exceeding a selected threshold. The grid resolution selection problem is solved, and the computational load is reduced a lot in the meantime. After the mixing matrix is obtained, the sources are recovered with -norm optimization. Numerical simulation and a speech separation application illustrate the superior performance of the proposed algorithm.

关键词： sparse component analysis Blind source separation Hough transform Grid resolution

来源：评论

学校读者我要写书评

暂无评论

sparse component analysis Using Time-Frequency Representations for Operational Modal analysis

引用

SENSORS 2015年第3期15卷 6497-6519页

作者： Qin, Shaoqian Guo, Jie Zhu, Changan Univ Sci & Technol China Dept Precis Machinery & Precis Instrumentat Hefei 230027 Peoples R China

sparse component analysis (SCA) has been widely used for blind source separation(BSS) for many years. Recently, SCA has been applied to operational modal analysis (OMA), which is also known as output-only modal identification. This paper considers the sparsity of sources' time-frequency (TF) representation and proposes a new TF-domain SCA under the OMA framework. First, the measurements from the sensors are transformed to the TF domain to get a sparse representation. Then, single-source-points (SSPs) are detected to better reveal the hyperlines which correspond to the columns of the mixing matrix. The K-hyperline clustering algorithm is used to identify the direction vectors of the hyperlines and then the mixing matrix is calculated. Finally, basis pursuit de-noising technique is used to recover the modal responses, from which the modal parameters are computed. The proposed method is valid even if the number of active modes exceed the number of sensors. Numerical simulation and experimental verification demonstrate the good performance of the proposed method.

关键词： sparse component analysis blind source separation operational modal analysis single source points

来源：评论

学校读者我要写书评

暂无评论

sparse component analysis-based under-determined blind source separation for bearing fault feature extraction in wind turbine gearbox

引用

IET RENEWABLE POWER GENERATION 2017年第3期11卷 330-337页

作者： Hu, Chun-zhi Yang, Qiang Huang, Miao-ying Yan, Wen-jun Zhejiang Univ Coll Elect Engn Hangzhou 310027 Peoples R China

The signal processing-based bearing fault diagnosis in wind turbine gearbox is considered challenging as the vibration signals collected from acceleration transducers are, in general, a mixture of signals originating from an unknown number of sources. Even worse, the source number is often larger than the number of installed sensors, and hence the fault characterisation is effectively an under-determined blind source separation problem. In this study, a novel sparse component analysis-based algorithmic solution is proposed to address this technical challenge from two aspects: source number estimation and source signal recovery, to enable accurate and efficient bearing fault diagnosis. The source number estimation is implemented based on the empirical mode decomposition and singular value decomposition joint approach. The observed signals are transformed to the time-frequency domain using short-time Fourier transform to obtain the sparse representation of the signals. The fuzzy C-means clustering and l(1) norm decomposition methods are used to estimate the mixing matrix and recover the source signals, respectively. The proposed solution is assessed through simulation experiments for scenarios of linearly and non-linearly mixed bearing vibration signals, and the numerical result confirms the effectiveness of the proposed algorithmic solution

关键词： wind turbines gears blind source separation feature extraction fault diagnosis transducers singular value decomposition time-frequency analysis Fourier transforms signal representation fuzzy systems wind turbine gearbox sparse component analysis under-determined blind source separation bearing fault feature extraction signal processing-based bearing fault diagnosis acceleration transducers source number estimation source signal recovery empirical mode decomposition singular value decomposition time–frequency domain short-time Fourier transform sparse signal representation fuzzy C-means clustering l1 norm decomposition nonlinearly mixed bearing vibration signals source estimation signal feature characterisation

来源：评论

学校读者我要写书评

暂无评论

sparse component analysis based on an improved ant K-means clustering algorithm for underdetermined blind source separation 16

Sparse component analysis based on an improved ant K-means c...

引用

16th IEEE International Conference on Networking, Sensing and Control (ICNSC)

作者： Wei, Shuang Wang, Feng Jiang, Defu Shanghai Normal Univ Coll Informat Mech & Elect Engn Shanghai Peoples R China Hohai Univ Coll Comp & Informat Array & Informat Proc Lab Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781728100845

This paper proposed an improved sparse component analysis (SCA) approach to improve the performance of underdetermined blind source separation for the acoustic/speech sources. First, the pre-processing to build a sparse model for acoustic/speech sources is described. Then, the proposed SCA approach designs an improved K-means clustering algorithm based on ant colony algorithm to estimate the mixture matrix, and utilize a method based on orthogonal matching pursuit algorithm to recover the signals. Experiment results demonstrate that the improved clustering algorithm can enhance the global searching ability which benefits for the proposed SCA approach to achieve a higher estimation accuracy of mixture matrix, and the improved method in the second step can make the proposed SCA approach work well for recovering the multi-channel blind source signals.

关键词： Underdetermined blind source separation sparse component analysis ant colony K-means clustering

来源：评论

学校读者我要写书评

暂无评论

sparse component analysis VIA DYADIC CYCLIC DESCENT

SPARSE COMPONENT ANALYSIS VIA DYADIC CYCLIC DESCENT

引用

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

作者： Ulfarsson, M. O. Solo, V. Univ Iceland Dept Elect Engn Reykjavik Iceland

ISBN: (纸本)9781479928934

sparse component analysis (SCA) is a widely used method for solving the blind source separation problem. We develop a new cyclic descent algorithm for SCA based on a dyadic expansion. To select the associated tuning parameter a method based on the Bayesian information criterion is developed. In simulations the new algorithm is compared with state of the art algorithms from the literature.

关键词： sparse component analysis Sparsity Cyclic Descent

来源：评论

学校读者我要写书评

暂无评论

sparse component analysis Based on Support Vector Machine for Fault Diagnosis of Roller Bearings

Sparse Component Analysis Based on Support Vector Machine fo...

引用

International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)

作者： Tang, Gang Li, Guozheng Wang, Huaqing Beijing Univ Chem Technol Coll Mech & Elect Engn Beijing Peoples R China

ISBN: (纸本)9781509040209

In order to improve the separation performance of blind source separation, a sparse component analysis method based on support vector machine is proposed. Firstly, the sample points of the observed composite signals are selected by calculating the direction angle of the composite signal as standard to discard the interference points. And the selected sample points are trained using support vector machine method. Then the optimal classification plane is determined to classify the observed signals. In addition, the mixed matrix is estimated by means of weighted summation of the confidence intervals. Finally, the source signals are separated based on shortest path method. Experimental results manifest the proposed method can extract the fault signal of rotating machinery successfully.

关键词： sparse component analysis mixed matrix estimation support vector machine

来源：评论

学校读者我要写书评

暂无评论

sparse component analysis in presence of noise using an iterative EM-MAP algorithm

引用

7th International Conference on Independent component analysis and Signal Separation

作者： Zayyani, Hadi Babaie-Zadeh, Massoud Mohimani, G. Hosein Jutten, Christian Sharif Univ Technol ACRI Dept Elect Engn Tehran Iran Inst Natl Polytech Grenoble Dept Images & Signals GIPSA Lab Grenoble France

ISBN: (纸本)9783540744931

In this paper, a new algorithm for source recovery in under-determined sparse component analysis (SCA) or atomic decomposition on over-complete dictionaries is presented in the noisy case. The algorithm is essentially a method for obtaining sufficiently sparse solutions of under-determined systems of linear equations with additive Gaussian noise. The method is based on iterative Expectation-Maximization of a Maximum A Posteriori estimation of sources (EM-MAP) and a new steepest-descent method is introduced for the optimization in the M-step. The solution obtained by the proposed algorithm is compared to the minimum l(1)-norrn solution achieved by Linear Programming (LP). It is experimentally shown that the proposed algorithm is about one order of magnitude faster than the interior-point LP method, while providing better accuracy.

关键词： sparse component analysis sparse decomposition blind source separation independent component analysis

来源：评论

学校读者我要写书评

暂无评论

sparse component analysis for Speech Recognition in Multi-Speaker Environment

Sparse Component Analysis for Speech Recognition in Multi-Sp...

引用

11th Annual Conference of the International-Speech-Communication-Association 2010

作者： Asaei, Afsaneh Bourlard, Herve Garner, Philip N. Idiap Res Inst Martigny Switzerland

ISBN: (纸本)9781617821233

sparse component analysis is a relatively young technique that relies upon a representation of signal occupying only a small part of a larger space. Mixtures of sparse components are disjoint in that space. As a particular application of sparsity of speech signals, we investigate the DUET blind source separation algorithm in the context of speech recognition for multiparty recordings. We show how DUET can be tuned to the particular case of speech recognition with interfering sources, and evaluate the limits of performance as the number of sources increases. We show that the separated speech fits a common metric for sparsity, and conclude that sparsity assumptions lead to good performance in speech separation and hence ought to benefit other aspects of the speech recognition chain.

关键词： sparse component analysis overlapping speech speech recognition

来源：评论

学校读者我要写书评

暂无评论

A criterion for the construction of a regularization function in sparse component analysis

引用

CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2005年第4期24卷 315-325页

作者： Du, XY Hu, WD Yu, WX Natl Univ Def Technol ATR Key Lab Changsha 410073 Peoples R China

With the application of regularization, sparse component analysis (SCA) becomes an effective approximate method for finding the sparsest solution of signal decomposition in an overcomplete dictionary. In this paper, the authors present some conditions on a family of regularization functions indexed by a hyperparameter so that these functions are applicable and can be effectively optimized in SCA. For a given signal, with these conditions it is proven that there exists at least one hyperparameter such that the global minimum of the corresponding regularization function can theoretically lead to the sparsest representation of the signal. Based on these propositions, a general principle is presented for the construction of regularization functions, and several kinds of function families are recommended for the purpose of sparse signal representation. The paper gives a numerical example that indicates that, for a synthesized signal, minimizing the regularization function proposed in this paper provides the correct sparse solution, whereas the method of basis pursuit fails.

关键词： sparse component analysis signal representation supperresolution regularization

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：