Description: 用于混响背景语音分离的结构稀疏模型(Strutured sparisty model)方法-To further tackle the ambiguity
of the reflection ratios, we propose a novel formulation of the
reverberation model and estimate the absorption coefficients
through a convex optimization exploiting joint sparsity model
formulated upon spatio-spectral sparsity of concurrent speech
representation. The acoustic parameters are then incorporated
for separating individual speech signals through either structured
sparse recovery or inverse filtering the acoustic channels.
The experiments conducted on real data recordings of spatially
stationary sources demonstrate the effectiveness of the proposed
approach for speech separation and recognition. Platform: |
Size: 1575936 |
Author:bigbigtom |
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Description: 张量分解提取生物学特征，NFEA: Tensor Toolbox for Feature
Extraction and Applications-
Data in modern applications such as BCI based on EEG signals often contain multi-modes due to
mechanism of data recording, e.g. signals recorded by multiple-sensors (electrodes), in multiple trials,
epochs, for multiple subjects and with different tasks, conditions. Moreover, during processing and
analysis, dimensionality of the data could be augmented due to expression of the data into sparse
domain (time-frequency representation) by different transforms such as STFT, wavelets. That means
data itself is naturally a tensor, and has multilinear structures. Standard approaches which analyze
such data by considering them as vectors or matrices might be not suitable due to risk of losing the
covariance information among various modes. To discover hidden multilinear structures, features
within the data, the analysis tools should reflect the multi-dimensional structure of the data Platform: |
Size: 2438144 |
Author:李新会 |
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Description: gabor原子库的产生，应用与核磁共振信号的稀疏表示和重建。-Generating a sparse, the application of nuclear magnetic resonance signal representation library gabor atom and reconstruction. Platform: |
Size: 1024 |
Author:唐学伟 |
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Description: 本书介绍了稀疏和冗余表示,内容分两部分,共十六章。第一部分是稀疏和冗余表示的理论和数值基础,系统、条理地罗列了数据模型的理论基础、相关算法的数值部分(This book introduces the sparse and redundant representation, the content is divided into two parts, a total of sixteen chapters. The first part is the theoretical and numerical foundation of sparse and redundant representation, and systematically and systematically lists the theoretical basis of the data model and the numerical value of the related algorithms) Platform: |
Size: 78229504 |
Author:LSB
|
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Description: Linear prediction (LP) is an ubiquitous analysis
method in speech processing. Various studies have focused on
sparse LP algorithms by introducing sparsity constraints into
the LP framework. Sparse LP has been shown to be effective in
several issues related to speech modeling and coding. However,
all existing approaches assume the speech signal to be minimumphase. Because speech is known to be mixed-phase, the resulting
residual signal contains a persistent maximum-phase component.
The aim of this paper is to propose a novel technique which
incorporates a modeling of the maximum-phase contribution of
speech, and can be applied to any filter representation. The
proposed method is shown to significantly increase the sparsity
of the LP residual signal and to be effective in two illustrative
applications: speech polarity detection and excitation modeling. Platform: |
Size: 268288 |
Author:pashaa
|
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Description: K-SVD可以看做K-means的一种泛化形式，K-means算法总每个信号量只能用一个原子来近似表示，而K-SVD中每个信号是用多个原子的线性组合来表示的。
K-SVD通过构建字典来对数据进行稀疏表示，经常用于图像压缩、编码、分类等应用。(K-SVD can be regarded as a generalized form of K-means. The total K-means algorithm can only approximate one signal for each semaphore, and each signal in K-SVD is a linear combination of multiple atoms To express.
K-SVD through the construction of the dictionary to sparse data representation, often used for image compression, encoding, classification and other applications.) Platform: |
Size: 625664 |
Author:华仔007
|
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Description: 利用信号本身的结构特征，通过附加不同的稀疏约束，该模型利用过完备字典进行信号分解，使其表示成字典中若干原子的线性组合，从而获得数据的精简表示。(By using the structural characteristics of the signal itself and adding different sparse constraints, the model decomposes the signal into linear combinations of atoms in the dictionary by using an over-complete dictionary, thus obtaining a simplified representation of the data.) Platform: |
Size: 4096 |
Author:tyc56 |
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Description: 本资源是3层的自编码器加上稀疏正则项约束的matlab代码。隐层激活函数选sigmoid函数，输出层选线性函数，程序中以一个标准数据集sonar为例，使用该方法可以做无监督表征学习，数据压缩，多任务学习等(This resource is a 3-layer self-encoder plus matlab code for sparse regular term constraints. The hidden layer activation function selects the sigmoid function, and the output layer selects the linear function. The program uses a standard data set sonar as an example. This method can be used for unsupervised representation learning, data compression, multi-task learning, etc.) Platform: |
Size: 101376 |
Author:帝都007 |
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Search - Sparse Representation