搜索资源列表
BP--for-classification
- MATLAB智能算法案例分析—— BP神经网络的数据分类-语音特征信号分类-MATLAB intelligent algorithm Case Study- BP neural network data classification- the speech characteristic signal classification
BPNN
- 采用matlab实现BP神经网络的数据分类-语音特征信号分类-BP neural network using matlab realize data classification- the speech characteristic signal classification
pluslr
- 使用了MATLAB对信号进行特征选择,使用的信号有图像信号,一维信号等。对学习信号特征有参考价值。-Using the MATLAB signal for feature selection, use of image signal of the signals, and the one dimensional signal, etc.Characteristics of learning signal has a reference value.
BP-CLASSIFY-OF-SIGNAL
- MATLAB神经网络30例书课后附的案例源程序 BP神经网络的数据分类-语音特征信号分类-MATLAB neural network 30 cases of after-school book case attached source BP neural network data classification- the speech characteristic signal classification
MATLAB-program
- 实现对信号特征的提取,能够实现对数据谱的峰值特征的提取。-To achieve the extraction of signal characteristics, enabling the extraction of data spectral peak features.
1
- 一个MATLAB代码,关于BP神经网络的数据分类,本例可用于语音特征信号分类-A MATLAB code, the data classification of BP neural network, the present embodiment may be used to classify the speech characteristic signal
matlab
- 对信号进行时域特征提取,个人已使用,感觉很不错!-The time domain feature extraction of the signal, the individual has been used, I feel very good!
STFFTtool
- 对非平稳信号进行分段、截取,将非平稳信号转化为平稳信号,再做短时傅里叶变换,分析其频谱特性,并找到各个时间节点的频率特性,以便分析不同事件段内的声音特征。(The non-stationary signals are segmented and intercepted, and then the short-time Fourier transform is used to analyze their frequency spectrum characteristics, and the fre
STFFTtool1
- 对非平稳信号进行分段、截取,再对处理过后的短时信号做短时傅里叶变换,分析其频谱特性,并找到各个时间节点的频率特性,以便分析不同事件段内的声音特征。(The nonstationary signals are segmented, and then the interception, the short-time signal after processing the short-time Fourier transform, the analysis of the spectrum charac
STFFTtool2
- 对非平稳信号进行分段、截取,再对处理过后的短时信号做短时傅里叶变换,分析其频谱特性,并找到各个时间节点的频率特性,以便分析不同事件段内的声音特征,达到区分不同声音的目的。(The nonstationary signals are segmented, and then the interception, the short-time signal after processing the short-time Fourier transform, the analysis of the spe
STFFTtool3
- 对非平稳信号进行分段、截取,再对处理过后的短时信号做短时傅里叶变换,分析其频谱特性,并找到各个时间节点的频率特性,以便分析不同事件段内的声音特征,一图像的形式表达出,最终达到区分不同声音的目的。(The nonstationary signals are segmented, and then the interception, the short-time signal after processing the short-time Fourier transform, the analysi
keypoint
- 对非平稳信号进行分段、截取,再对处理过后的短时信号做短时傅里叶变换,分析其频谱特性,并找到各个时间节点的频率特性,以便分析不同事件段内的声音特征,以图像的形式表达出,最终达到区分不同声音的目的。(The nonstationary signals are segmented, and then the interception, the short-time signal after processing the short-time Fourier transform, the analysi
MATLAB1
- matlab信号特征,最大值,最小值,熵(Signal feature extraction)
AE Calculation
- 个人编写的计算单次声发射信号的时域特征参数的程序,用以计算声发射信号的幅值,上升时间,持续时间,计数,能量等(The matlab program is used for calculating the characteristics of a burst AE signal in Time domian, such as amplitude, rise time, duration, count, energy and so on.)
doa_music
- MUSIC算法是一种基于矩阵特征空间分解的方法。从几何角度讲,信号处理的观测空间可以分解为信号子空间和噪声子空间,显然这两个空间是正交的。信号子空间由阵列接收到的数据协方差矩阵中与信号对应的特征向量组成,噪声子空间则由协方差矩阵中所有最小特征值(噪声方差)对应的特征向量组成。(MUSIC algorithm is a kind of feature space based on the matrix decomposition method.From geometric point of vie
YCL
- 对信号进行梅尔倒谱系数特征采集之前的预处理,包括分帧加窗、基于短时能量的双门限判别、除噪、预加重等(The preprocessing of Mel cepstrum coefficients before the signal is carried out, including frame windowing, double threshold discrimination based on short-time energy, denoising and pre emphasis)
新建文件夹
- ICA(独立成分分解),可实现采集信号中源信号的分离,便于提取特征量,实现模式识别。同时由于可以将源信号分离开来,可实现信号的降噪,去掉基波、三次谐波、五次谐波等(ICA (independent component decomposition) can realize the separation of the source signals in the acquisition signal, and facilitate the extraction of the feature quant
matlab小波变换程序
- 小波变换(wavelet transform,WT)是一种新的变换分析方法,它继承和发展了短时傅立叶变换局部化的思想,同时又克服了窗口大小不随频率变化等缺点,能够提供一个随频率改变的"时间-频率"窗口,是进行信号时频分析和处理的理想工具。它的主要特点是通过变换能够充分突出问题某些方面的特征,能对时间(空间)频率的局部化分析,通过伸缩平移运算对信号(函数)逐步进行多尺度细化,最终达到高频处时间细分,低频处频率细分,能自动适应时频信号分析的要求,从而可聚焦到信号的任意细节,解决了
package_emd
- 经验模态分解(Empirical Mode Decomposition,EMD)法是黄锷(N. E. Huang)在美国国家宇航局与其他人于1998年创造性地提出的一种新型自适应信号时频处理方法,特别适用于非线性非平稳信号的分析处理。对经过EMD处理的信号再进行希尔伯特变换,就组成了大名鼎鼎的“希尔伯特—黄变换”(HHT)。由于脑电信号处理很少在EMD之后接上希尔伯特变换,在这里仅介绍EMD的相关基础知识。 EMD其实就是一种对信号进行分解的方法,与傅里叶变换、小波变换的核心思想一致,大家
LPCC
- 线性预测倒谱系数(Linear Prediction Cepstrum Coefficient,LPCC)是线性预测系数(Linear Prediction Coefficient,LPC)在倒谱域中的表示。该特征是基于语音信号为自回归信号的值设,利用线性预测分析获得倒谱系数。(Linear Prediction Cepstrum Coefficient)