搜索资源列表
dct_cs
- 采用BP算法来实现压缩感知的信号重构示例。BP算法由线性规划来实现，稀疏基为DCT基，信号为语音信号-an example of using BP algorithm for signal reconstruction in compressed sensing. BP algorithm is implemented by linear programming, sparse basis is the DCT basis, the signal used is speech
ogrady2007_phd
- 国外欠定语音盲分离的博士论文，作者为Paul D. O’Grady，LOST算法的作者。该博士论文包括语音信号分离，非负矩阵分解等内容。-Sparse Separation of Under-Determined Speech Mixtures，A dissertation submitted for the degree of Doctor of Philosophy
ksvd_train_for_mix
- 用ksvdbox12来构造反映源语音特性的稀疏过完备基示例。训练信号为两个说话人的语音信号。-Use the ksvdbox12 to construct sparse over-complete basis reflecting the characteristics of the training signals. Training signals used in the example are speech signals of two speakers.
mean-K-KPCA
- 通过核 K- 均值聚类的方法对语音帧进行聚类 , 由于聚类的中心能够很好地代表类内的特征, 用中心样本帧取代该类, 减少了核矩阵的维数, 然后再采用稀疏 KPCA方法对核矩阵进行特征提取。-Through the nuclear K-means clustering method for clustering of speech frames, the cluster center can be a good representative of the class characteristics
sparse-coding-and-idbm
- 维纳滤波、理想二进制掩膜以及理想二进制掩膜和稀疏编码相结合的语音加强-speech enhancement algorithms ,including wiener filter , IBM and research on IBM and sparse coding jointly
PCA
- 针对稀疏表示识别方法需要大量样本训练过完备字典且特征冗余度较高的问题，提出了结合过完备字典学习与PCA降维的小样本语音情感识别算法.该方法首先用PCA降维方法将特征降维，再将处理后的特征用于过完备字典训练与稀疏表示识别方法，从而给出了语音情感特征的稀疏表示方法，并确定了新算法的具体步骤.为验证其有效性，在同等特征维数下，将方法与BP, SVM进行比较，并对比、分析语音情感特征稀疏化前后对语音情感识别率、时间效率以及空间效率的影响.试验结果表明，所提出方法的识别率比SVM与BP高 与采用稀疏化前的
compress_sensing_without_frame
- Compressive sampling is an emerging technique that promises to effectively recover a sparse signal from far fewer measurements than its dimension. The compressive sampling theory assures almost an exact recovery of a sparse signal if the signal is se
larc-of-speech-enhancement
- 基于字典的语音增强中稀疏编码计算稀疏矩阵的一种改进算法，称作larc-Dictionary-based computing sparse coding speech enhancement sparse matrix, an improved algorithm, called larc
KSVD-of-speech-enhancemant
- 基于字典学习的语音增强中字典更新的算法，称作近似K-SVD算法，其中包含了OMP算法用于稀疏编码计算系数矩阵-Dictionary-based learning dictionary speech enhancement algorithm update, called approximate K-SVD algorithm, which contains the sparse coding algorithm is used to calculate the coefficient matri
omp-column
- 基于字典学习的语音增强中稀疏编码计算稀疏矩阵的一种算法，称作OMP。与一般的OMP不同，本程序针对列向量进行计算，结合给出的总体程序以及KSVD of speech enhancemant.rar文件可以进行字典学习语音增强。-Enhanced sparse coding algorithm to calculate a sparse matrix, called the dictionary-based learning OMP voice. OMP different with the ge
SparseDeconv_software
- 基于1范数的稀疏信号反卷积算法的程序代码，语音信号、迭代算法-The sparse signal deconvolution algorithm based on 1 norm of program code, speech signal, an iterative algorithm
sons
- Compressive sensing (CS) has been proposed for signals with sparsity in a linear transform domain. We explore a signal dependent unknown linear transform, namely the impulse response matrix operating on a sparse excitation, as in the linear mod
Feature-Denoising
- joint sparse representation (JSR)方法用于车内语音增强的特征降噪算法-address reducing the mismatch between training and testing conditions for hands-free in-car speech recognition. It is well known that the distortions caused by background noise, channel effec
Structured-Sparsity-Models
- 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
Speech-Denoising-master
- 语音增强，基于数据融合的语音增强方法，用来处理稀疏噪声-Speech enhancement, speech enhancement based on data fusion method, used to deal with sparse noise
sreenivas2009-icassp
- Compressive sensing (CS) has been proposed for signals with sparsity in a linear transform domain. We explore a signal dependent unknown linear transform, namely the impulse response matrix operating on a sparse excitation, as in the linear model of
SPL-MaxLP-IEEE Letter2016
- 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 issu
speech reconstruction+SLP
- This paper proposes a new variant of the least square autoregressive (LSAR) method for speech reconstruction, which can estimate via least squares a segment of missing samples by applying the linear prediction (LP) model of speech. First, we show t
speech coding based on SLP-2009
- This paper describes a novel speech coding concept created by introducing sparsity constraints in a linear prediction scheme both on the residual and on the prediction vector. The residual is efficiently encoded using well known multi-pulse excitat
pd2
- A Sparse Representation-Based Wavelet Domain Speech Steganography Method