Description: This a Bayesian ICA algorithm for the linear instantaneous mixing model with additive Gaussian noise [1]. The inference problem is solved by ML-II, i.e. the sources are found by integration over the source posterior and the noise covariance and mixing matrix are found by maximization of the marginal likelihood [1]. The sufficient statistics are estimated by either variational mean field theory with the linear response correction or by adaptive TAP mean field theory [2,3]. The mean field equations are solved by a belief propagation method [4] or sequential iteration. The computational complexity is N M^3, where N is the number of time samples and M the number of sources. Platform: |
Size: 7168 |
Author:陈互 |
Hits:
Description: 用外积和法设计的权矩阵，不能保证p对模式全部正确的联想。若对记忆模式对加以限制（即要求p个记忆模式Xk是两两正交的），则用外积和法设计的BAM网具有较好的联想能力。 在难以保证要识别的样本（或记忆模式）是正交的情况下，如何求权矩阵，并保证具有较好的联想能力？这个问题在用BAM网络实现对字符的识别程序仿真中得到体现。我们做过尝试，用伪逆法求权矩阵，虽然能对未加干扰的字符全部进行识别，但对加有噪声的字符识别效果很差。至于采用改变结构和其他算法的方法来求权矩阵，将是下一步要做的工作。-foreign plot and the design of the power matrix, p is no guarantee that all the correct pattern association. If memory model, the limit (that is, p-memory model Xk is orthogonal to the February 2), then foreign plot and design of the BAM network has good ability to think. It is difficult to ensure the samples to identify (or memory mode) is orthogonal circumstances, the right to seek ways matrix, and to ensure that the association has good ability? The problem with the BAM network of characters identification procedures simulation can be manifested. We did try to use pseudo- inverse matrix for the right, although they would not increase the interference of the characters in the identification of all, However, a pair of noise increases the effects of poor character recognition. As for the Platform: |
Size: 231424 |
Author:东方云 |
Hits:
Description: Generate 100 samples of a zero-mean white noise sequence with variance , by using a uniform random number generator.
a Compute the autocorrelation of for .
b Compute the periodogram estimate and plot it.
c Generate 10 different realizations of , and compute the corresponding sample autocorrelation sequences , and . Compute the average autocorrelation sequence as and the corresponding periodogram for .
d Compute and plot the average periodogram using the Bartlett method.
e Comment on the results in parts (a) through (d).
-Generate 100 samples of a zero-mean white noise sequence with variance, by using a uniform random number generator.a Compute the autocorrelation of for. B Compute the periodogram estimate and plot it. C Generate 10 different realizations of, and compute the corresponding sample autocorrelation sequences, and. Compute the average autocorrelation sequence as and the corresponding periodogram for. d Compute and plot the average periodogram using the Bartlett method. e Comment on the results in parts (a) through (d). Platform: |
Size: 1024 |
Author:冀晗 |
Hits:
Description: 是matlab源码，利用bp网络实现0～9数字识别系统，友好的系统界面，包括训练样本和含噪声的数字图片。-Matlab source code is the use of network bp realize 0 ~ 9 digital identification system friendly system interface, including the training samples and the number of images containing noise. Platform: |
Size: 5120 |
Author:李秀艳 |
Hits:
Description: 这个例子里，我们将独立(白噪声)样本通过有5dB通带纹波的切比雪夫滤波器。
试估计滤波器输出端的PSD。实现这一估计的MATLAB程序如下:
-This example, we will be independent (white noise) samples through a 5dB passband ripple Chebyshev filter. Estimate the filter output PSD. MATLAB program to achieve this estimate is as follows: Platform: |
Size: 1024 |
Author:defang |
Hits:
Description: 对噪声信号中的正弦信号，通过Pisarenko谐波分解方法、Music算法和Esprit算法进行频率估计，信号源是： 其中， ， ， ； 是高斯白噪声，方差为 。使用128个数据样本进行估计。 1、用三种算法进行频率估计，独立运行20次，记录各个方法的估计值，计算均值和方差； 2、增加噪声功率，观察和分析各种方法的性能。-Sinusoidal signal in the noise signal through the Pisarenko harmonic decomposition method, Music algorithms and Esprit frequency estimation algorithm, the signal source is: where,,, a Gaussian white noise, variance. 128 data samples used to estimate. 1, with three kinds of frequency estimation algorithm, run independently 20 times, record the estimated value of each method to calculate the mean and variance 2, the increase in noise power, observation and analysis of the performance of various methods. Platform: |
Size: 2048 |
Author:gab |
Hits:
Description: Matlab的BP网络字符识别
只用理想样本训练了，大家用的时候可以用加噪后的再进行训练即可
-BP network character recognition Matlab' s the ideal training samples only, and everyone can use when adding noise to the training again after the Platform: |
Size: 1024 |
Author:pipi |
Hits:
Description: 是matlab源码，运用bp网络实现0～9数字识别系统，友友好的系统界面，包含训练样本与含噪声的数字图片。 可直接使用。
-Matlab source code, bp network 0 to 9 digit recognition system, Friends of the friendly system interface, including the training samples and digital images with noise. Can be used directly. Platform: |
Size: 6144 |
Author: |
Hits:
Description: In this paper the analysis of the compression process was performed by comparing the compressed signal against the original signal. To do this the most powerful speech analysis and compression techniques such as Linear Predictive Coding (LPC) and Discrete Wavelet Transform (DWT) was implemented using MATLAB. Here nine samples of spoken words are collected from different speakers and are used for implementation. The results obtained from LPC were compared with other compression technique called Discrete Wavelet Transform. Finally the results were evaluated in terms of compressed ratio (CR), Peak signal-to-noise ratio (PSNR) and Normalized root-mean square error (NRMSE).The result shows that DWT performance was better for these samples than the LPC method. Platform: |
Size: 147456 |
Author:Ambika |
Hits:
Description: 根据人声及鸟声频率的不同，将两种声音分离出来。-talk_sound.wav is a mono (1-channel) 44,100 samples per sec, 16-bit per sample WAV file.
Write a Matlab program to separate the voice from the background sounds and to separate the background sounds from the voice. The voice occupies mainly 900 Hz and below, the background sound is both noise and bird chirps that tend to be within 1000-1400 Hz.
Write out the separated voice and the separated background to separate wav files titled voice.wav and background.wav.
Platform: |
Size: 918528 |
Author:张小云 |
Hits:
Description: 假设用图示所示的两个正交信号经由一个AWGN信道传输二进制信息，在持续期Tb的每个比特区间接收到的信号以10/Tb速率采样，即每个比特区间内10个样本，幅度为A。噪声是均值为零，方差为 的高斯过程。
写MATLAB程序，在方差为0,0.1,1.0和2.0时，完成接收信号和两种发射信号的每一种的离散时间相关，画出在时刻k=1,2,…,10相关器的输出。-Assuming an AWGN channel transmission via binary information in two orthogonal signals icon shown in the ratio of each signal received indirect SAR duration Tb to 10/Tb sampling rate, that is, within the range 10 samples per bit, amplitude A. Noise is zero mean and variance of the Gaussian process.
Write a MATLAB program, the variance is 0,0.1,1.0 and 2.0, to complete each of the two discrete-time signals and transmit the received signal correlation shown in the time k = 1,2, ..., 10 output of the correlator . Platform: |
Size: 2048 |
Author:卢昳丽 |
Hits:
Description: Matlab仿真：采样次数与信号的信噪比的关系曲线图。-Matlab simulation: the number of samples and the signal to noise ratio of a plot. Platform: |
Size: 1024 |
Author:swet2010 |
Hits:
Description: 这个MATLAB构建一个锻炼LPC声码器,即,执行LPC分析和合成语音文件,导致合成语音近似原始的演讲。LPC分析使用一个标准的自相关分析来确定LPC系数的设置,一帧一帧的基础上,以及框架获得。一个独立的分析方法(cepstral螺距内检测器)把每一帧的言论是要么表示演讲(时间由cepstral峰值的位置在指定范围的音调时期)或无声的言论(模拟随机噪声帧)0帧基音周期的样本。独立的分析提供了一个两国并存的激发函数LPC合成处理的一部分,包括一系列的脉冲(表示帧期间)和/或噪声序列(在无声的帧)。-This MATLAB exercise builds an LPC vocoder, i.e., performs LPC analysis and synthesis on a speech file, resulting in a synthetic speech approximation to the original speech. The LPC analysis uses a standard autocorrelation analysis to determine the sets of LPC coefficients, on a frame-by-frame basis, along with the frame-based gain. An independent analysis method (a cepstral pitch period detector) classifies each frame of speech as being either voiced speech (with period determined by the location of the cepstral peak in a designated range of pitch periods) or unvoiced speech (simulated by a random noise frame) with a frame pitch period of 0 samples. The independent analysis provides a two-state excitation function for the LPC synthesis part of the processing, consisting of a series of pitch pulses (during voiced frames) and/or noise sequences (during unvoiced frames). The file 5.13 LPC Vocoder.pdf provides a User s Guide for this exercise. Platform: |
Size: 2496512 |
Author:wujin |
Hits:
Search - samples noise matlab