Description: 关于谱估计方面的重要函数,能很好的估计出信号的谱,质量很高-on spectral estimation of the important functions can be a very good signal to estimate the spectrum of high quality Platform: |
Size: 1777 |
Author:xjs |
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Description: This paper presents various applications of a nonlinear
adaptive notch filter which operates based on the concept
of an enhanced phase-locked loop (PLL). Applications of the filter
for online signal analysis for power systems protection, control and
power quality enhancement are presented. The proposed scheme
can be applied for signal analysis both under stationary and nonstationary
conditions. Based on digital time-domain simulations,
applications of the filter for a) sinusoidal waveform peak detection,
b) harmonic identification/detection, c) detection/extraction
of individual components of a signal, d) instantaneous reactive
current extraction, e) disturbance detection, f) noise reduction in
zero-crossings detection, and g) amplitude (phase) demodulation
for flicker estimation, are presented. Platform: |
Size: 153503 |
Author:yangyansky |
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Description: 关于谱估计方面的重要函数,能很好的估计出信号的谱,质量很高-on spectral estimation of the important functions can be a very good signal to estimate the spectrum of high quality Platform: |
Size: 1024 |
Author:xjs |
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Description: 语音信号的短时分析，主要包括：分帧、短时能量、短时平均幅度、短时过零率、短时自相关函数、短时幅度差、倒谱、复倒谱、lpc系数、lpc谱估计等
绝对保证质量，是保研后导师布置的一些基础程序-Short-time speech signal analysis, mainly including: sub-frame, short-time energy, short-term average, short-time zero-crossing rate, short-time auto-correlation function, short-term rate of poor cepstrum, complex cepstrum, lpc coefficients, lpc spectral estimation, such as an absolute guarantee that the quality of instructors is the security arrangement after the inquest some of the basis of procedures Platform: |
Size: 6144 |
Author:云鹏 |
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Description: SPEECH ENHANCEMENT BASED ON WAVELET DENOISING
Abstract: - Noise is an unwanted and inevitable interference in any form of communication. It is
non-informative and plays the role of sucking the intelligence of the original signal. Any kind of
processing of the signal contributes to the noise addition. A signal traveling through the channel
also gathers lots of noise. It degrades the quality of the information signal. The effect of noise
could be reduced only at the cost of the bandwidth of the channel which is again undesired as
bandwidth is a precious resource. Hence to regenerate original signal, it is tried to reduce the
power of the noise signal or in the other way, raise the power level of the informative signal, at
the receiver end this leads to improvement in the signal to noise ratio (SNR). There are several
ways in doing it and here the focus is on adaptive Signal processing new technique (Grazing
Estimation method) to improving the signal to noise ratio.-SPEECH ENHANCEMENT BASED ON WAVELET DENOISING
Abstract:- Noise is an unwanted and inevitable interference in any form of communication. It is
non-informative and plays the role of sucking the intelligence of the original signal. Any kind of
processing of the signal contributes to the noise addition. A signal traveling through the channel
also gathers lots of noise. It degrades the quality of the information signal. The effect of noise
could be reduced only at the cost of the bandwidth of the channel which is again undesired as
bandwidth is a precious resource. Hence to regenerate original signal, it is tried to reduce the
power of the noise signal or in the other way, raise the power level of the informative signal, at
the receiver end this leads to improvement in the signal to noise ratio (SNR). There are several
ways in doing it and here the focus is on adaptive Signal processing new technique (Grazing
Estimation method) to improving the signal to noise ratio. Platform: |
Size: 192512 |
Author:majid |
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Description: 随机信号谱分析技术实现 随机信号谱估计及质量评价。
离散随机信号通过线性时不变系统时，系统所产生的响应。
功率谱估计的实现方法：自相关函数法、周期图法、Bartlett法、Welch法、MTM法、MUSIC法
-Random signal spectral analysis of random signal spectral estimation and quality evaluation. Discrete random signals through linear systems, the system generated response. Realization of the power spectrum estimation: autocorrelation function, periodogram, Bartlett law, Welch method, MTM method, MUSIC method Platform: |
Size: 11264 |
Author:李倩 |
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Description: 本篇論文提出的整合式解交錯(Integrated De-interlacing)的演算法，可以有效提昇移
動區域的畫面，但是當移動估計不正確時，反而會使移動補償後的畫面變得很差，為了
改善這種情況，因此結合移動可適性解交錯的優點，並將空間圖場內插(Spatial
Interpolation)的方式改成ELA(Edge Line Average)來設計，經過電腦模擬的結果發現，不僅在視覺上提高畫面的解析度，在某些影像峰值訊號雜訊比(Peak Signal Noise Ratio ,
PSNR)也比線平均解交 錯(Line Average De-interlacing)多出好幾分貝的畫質增益。
此外，在整合式解交錯演算法中也增加影片偵測(Film Detection)和影像加強(Image
Enhancement)的演算法設計，在這樣演算法的架構下，透過影片偵測的演算法，我們可
真實地還原3:2 Pull Down 的影片格式，而不會有鋸齒狀(Saw-Toothed)的畫面出現，而影
像加強的演算法，則可以在解交錯後，經過影像的調整，使輸出畫面呈現不同的效果，
達到消費者的需求。-The main theme of this thesis is an integrated de-interlacing system, which incorporates
several known and improved techniques in a nice manner to produce good de-interlaced image
quality. We first develop an accurate motion detector that classifies image regions into
stationary, low-motion, and high-motion categories. The simple field merging method is
applied to the stationary regions. The edge line average interpolation method is applied to the
slow-motion regions. Finally, the motion-compensated interpolation is applied to the
high-motion regions. In addition, hierarchical motion estimation and motion vector smoothing
techniques are employed to enhance the quality of estimated motion vectors. Our computer
simulation shows that the subjective image quality is improved by using the proposed scheme.
Also, its PSNR measures are better than the conventional spatial or temporal interpolation
schemes. Platform: |
Size: 1171456 |
Author:robin |
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Description: 详细说明：语音信号的短时分析，主要包括：分帧、短时能量、短时平均幅度、短时过零率、短时自相关函数、短时幅度差、倒谱、复倒谱、lpc系数、lpc谱估计等 绝对保证质量，是保研后导师布置的一些基础程序-Details: short-time speech signal analysis, including: framing, short-term energy, short-term average rate, short-time zero crossing rate, short-time autocorrelation function, short-term magnitude difference, cepstrum, complex cepstrum, lpc coefficient, lpc absolute guarantee of the quality of spectral estimation, is the security arrangement of some of the research base after the mentor program Platform: |
Size: 6144 |
Author:林溪 |
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Description: 子空间类波达方向(Direction Of Arrival,DOA)估计算法的关键在于得到高质量的信号子空间估计。该文利用矩阵伪逆的双正交性,针对源信号不相关而其本身是色信号的情况,给出了一种新颖的DOA估计算法,它不需要知道噪声统计特性。该算法利用一组空间相关矩阵的结构化信息,能稳健而精确地估计出信号子空间,从而得到DOA的精确估计。仿真实验证实了所给算法的有效性。-The key subspace DOA (Direction Of Arrival, DOA) estimation algorithm is to get a high quality signal subspace estimation. In this paper, the use of pseudo-inverse matrix dual orthogonal signals for the source itself is not related to the case and the color signal is given a novel DOA estimation algorithm, it does not need to know the statistical properties of the noise. The algorithm uses a set of structured information in spatial correlation matrix can be robust and accurately estimate signal subspace to obtain an accurate estimate of the DOA. Simulation experiments confirmed the effectiveness of the proposed algorithm. Platform: |
Size: 306176 |
Author:yushuhua |
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Description: 基于压缩感知思想的图像分块压缩与重构方法
考虑到大多数图像信号信息分布有差异, 编码端, 在对图像分块的基础上, 融合熵估计
和边缘检测方法计算各图像块的信息含量, 再从两个不同的角度进行分类采样: 依据信息量多少将图像块分为平滑、过渡和纹理3 类, 使用不同的采样率采样 依据信息量的分布特征, 采用不同的采样率分配策略进行采样. 在解码端, 根据不同类型的图像块构造不同的线性算子进行重构, 再运用改
进的迭代阈值算法去除块效应和噪声. 实验证明, 算法在提升图像重构质量的同时缩短了重构时间,并且对纹理边缘多的图像的重构效果较其他方法理想.-Considering that most information of the image signal difference distribution, the encoding side, in the right image sub-blocks based on the integration entropy estimation
And edge detection method for calculating the information content of each image block, and two different angles classification sampling: The number of image blocks based on the amount of information into a smooth, transition and texture three classes, using different sampling rates based on the amount of information distribution characteristics, using different sampling sampling rate allocation strategy. in the decoding side, depending on the type of image blocks to construct different linear operator reconstructed, re-use change
Iterative thresholding algorithm into blockiness and noise removal experiments show image reconstruction algorithm to enhance the quality while reducing reconfiguration time, and much of the texture edge image reconstruction results are satisfactory compared to other methods. Platform: |
Size: 10092544 |
Author:Dr_wong |
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Description: In statistics and signal processing, a minimum mean square error (MMSE) estimator is an estimation method which minimizes the mean square error (MSE) of the fitted values of a dependent variable, which is a common measure of estimator quality. In the Bayesian setting, the term MMSE more specifically refers to estimation in a Bayesian setting with quadratic cost function. In such case, the MMSE estimator is given by the posterior mean of the parameter to be estimated. Since the posterior mean is cumbersome to calculate, the form of the MMSE estimator is usually constrained to be within a certain class of functions. Linear MMSE estimators are a popular choice since they are easy to use, calculate, and very versatile. It has given rise to many popular estimators such as the Wiener-Kolmogorov filter and Kalman filter Platform: |
Size: 1024 |
Author:nagendra |
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Description: The computation of the time delay of arrival (TDOA) between each of the considered channels and the reference channel is repeated along the recording in order for the beamforming to respond to changes in the speaker. In this implementation it is computed every 250ms (called segment size or analysis scroll) over a window of 500ms (called the analysis window) which covers the current analysis segment and the next. The size of the analysis window and of the segment size constitute a tradeoff. A big analysis window or segment window lead to a reduction in the resolution of changes in the TDOA. On the other hand, using a very small analysis window reduces the robustness of the cross-correlation estimation, as less acoustic frames are used to compute it. The reduction of the segment size also increases the computational cost of the system, while not increasing the quality of the output signal. Platform: |
Size: 1270784 |
Author:Saptian |
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Description: In statistics and signal processing, a minimum mean square error (MMSE) estimator is an estimation method which minimizes the mean square error (MSE) of the fitted values of a dependent variable, which is a common measure of estimator quality. In the Bayesian setting, the term MMSE more specifically refers to estimation in a Bayesian setting with quadratic cost function. In such case, the MMSE estimator is given by the posterior mean of the parameter to be estimated. Since the posterior mean is cumbersome to calculate, the form of the MMSE estimator is usually constrained to be within a certain class of functions. Linear MMSE estimators are a popular choice since they are easy to use, calculate, and very versatile. It has given rise to many popular estimators such as the Wiener-Kolmogorov filter and Kalman filter. Platform: |
Size: 1024 |
Author:Said |
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Search - signal quality estimation