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mfcc
- 在语音辨识（Speech Recognition）和语者辨识（Speaker Recognition）方面，最常用到的语音特征就是「梅尔倒频谱系数」（Mel-scale Frequency Cepstral Coefficients，简称MFCC），此参数考虑到人耳对不同频率的感受程度，因此特别适合用在语音辨识。
myVoron
- 对语音特征参数进行空间划分的函数，对采用VQ进行语音识别的设计的朋友朋友们很有用-Speech feature parameters for space partition function, to the design of using speech recognition of VQ friends friends is very useful
voice-recognition
- 3、语音特征提取与分类 首先, 待识别语音转化为电信号后输入识别系统, 经过预处理后用数学方法提取语音特征信号, 提取出的语音特征信号可以看成该段语音的模式。-3, the voice feature extraction and classification First, to be recognized voice into electrical signals input recognition system, after pretreatment with a mathematica
sharks_1.0
- 小波分析在语音特征参数提取方面的应用,很实用，经测试！-Wavelet analysis in terms of the voice feature extraction!
BP1111
- bp神经网络的数据分类-语音特征信号分类 bp神经网络的非线性系统建模-非线性函数的拟合-Linear systems neural network modeling of nonlinear systems, according to the classification of BP neural network genetic algorithm to optimize BP neural network
BP-SHENJINGWANGLOU
- BP 神经网络，有BP神经网络在函数逼近、语音特征信号分类、非线性等的应用-BP neural network, BP neural network approach has the function, speech characteristic signal classification and nonlinear applications
matlab
- BP神经网络的数据分类———语音特征信号分类 本案例选取了民歌、古筝、摇滚和流行四类不同音乐,用BP神经网络实现对这四类音乐 的有效分类。-Data speech characteristic signal classification of BP neural network, selects the guzheng, folk, rock and pop four different types of music, the realization of the four types
bpnerual
- BP神经网络的数据分类——————语音特征信号分类-Data BP neural network classification speech characteristic signal classification
MFCC
- MFCC算法实例及源代码实现，用于提取语音特征进行进一步对比-Example of MFCC algorithm and Realization of the source code, for extracting speech features for further comparison
Speech-Recognition
- 语音识别,基于matlab的语音特征对比分析-Speech Recognition
signal-classification
- 主要是通过BP神经网络的数据分类来实现语音特征信号分类-Mainly through data classification of BP neural network to realize speech characteristic signal classification
BPDLX
- 这是个BP神经网络的数据分类程序，此程序针对语音特征信号分类。-This is a BP neural network data classification program, the program for the speech characteristic signal classification.
mfcc
- 语音识别MFCC特征提取matlab代码。 「梅尔倒频谱系数」（Mel-scale Frequency Cepstral Coefficients，简称MFCC），是最常用到的语音特征，此参数考虑到人耳对不同频率的感受程度，因此特别适合用在语音辨识。-Speech recognition MFCC feature extraction matlab code. \ Mel cepstrum coefficient (Mel- scale Frequency Cepstral Coefficien
opensmile-source-1.0.1
- 该工具包为语音特征的自动提取工具 用于语音识别特征的提取 只需输入相应指令便可自动提取-Extraction Automatic extraction tool for voice recognition feature of the kit for the speech features simply enter the corresponding command will automatically extract
MFCC1
- 提取语音的MFCC特征参数，在语音识别（Speech Recognition）和话者识别（Speaker Recognition）方面，最常用到的语音特征就是梅尔倒谱系数（Mel-scale Frequency Cepstral Coefficients，简称MFCC）。-MFCC feature parameters extracted speech, speech recognition (Speech Recognition) and speaker verification (Speak
BP-network
- BP神经网络的数据分类-语音特征信号分类-BP neural network data classification- the speech characteristic signal classification
chapter1
- 运用BP神经网络进行分类识别，具体运用到语音特征信号进行分类。-BP neural network is used for classification and recognition, and the specific application to speech feature signal classification.
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
Voice Discern For STM32F
- 于市售 STM32 开发板上实现特定人语音识别处理项目。识别流程是：预滤波、ADC、分帧、端点检测、预加重、加窗、特征提取、特征匹配。端点检测(VAD)采用短时幅度和短时过零率相结合。检测出有效语音后，根据人耳听觉感知特性,计算每帧语音的 Mel 频率倒谱系数(MFCC)。然后采用动态时间弯折(DTW)算法与特征模板相匹配,最终输出识别结果。先用Matlab对上述算法进行仿真，经数次试验求得算法内所需各系数的最优值。而后将算法移植到 STM32 开发板上，移植过程中根据 STM32 上存储空间相
BP
- BP神经网络的语音特征信号分类算法，采用归一化算法及变学习率学习算法-BP neural network speech characteristic signal classification algorithm, using a normalization algorithm and the learning algorithm learning rate change