spectral frequency meaning in Chinese
光谱频率
谱频率
Examples
- After compared these methods , power spectral frequency band intensity , pca and linear network were choose to carry out the recognition system . finally , this paper provided software of gui as well as a group of simulink blocks to operate data and acquire results
在综合比较各种方法后,最终选择功率谱估计频带强度为分类特征, pca用于特征提取,线性神经网络作为分类器用于系统的实现。 - 4 different types ’ features were generated , namely ar model parameters , power spectral frequency band intensity , energy for wavelet packet decomposition , wavelet packet entropy . every type of features were extracted respectively using pca and ica method and classified using linear neural network , knn and bp network
建立了ar模型参数、功率谱估计频带强度、小波包分解能量比率、小波包熵四种特征,分别使用pca与ica进行特征提取,采用线性神经网络、 k -紧邻法、 bp神经网络四种分类器进行分类。 - The new algorithm has three characters : first is that the new algorithm is on the basis of super frame which include 3 continuous simple frames in melp algorithm , the algorithm deal with parameters of a super frame by the mode of the super frame . secondly , two algorithms are introduced for improving vector quantization quality of line spectral frequency ( lsf ) parameter . one is swithed - adaptive inter - frame vector prediction ( sivp ) , which can get rid of the correlation between neighboring frames effectively , the other is joint codebook optimization for multi stage vector quantization ( jco - msvq ) , which can improve performance of codebook
第二是在制作线谱对参数( lsf )矢量量化码本时,引入了目前提高lsf码本性能的两个方法:在利用重点帧对非重点帧作预测时,借鉴自适应帧间矢量量化( sivp )去除相关性算法的优点,提出了以固定矩阵去除相关性的方法,有效的控制了预测后残差的动态范围,提高了对残差矢量量化的精度;采用联合码本优化多级矢量量化算法( jco - msvq ) ,改良多级矢量量化码本的性能。