specgram_ex3p19

mfcc.m 用于语音识别上的mfcc特征参数提取函数。(mfcc.m mfcc characteristic parameters for speech recognition on the extraction function.)

频域的模态参数识别方法之一，可以对振动信号进行分析处理，得出结构的固有频率，阻尼比，振型系数，方法很好，程序完整(Frequency-domain modal parameter identification methods that can analyze the vibration signal processing, draw the structure of natural frequencies, damping ratio, mode shape coefficient, very good method, program integrity)

可对一段信号进行分帧加窗，利用过零率和短时能量检测语音端点(It can divide the frame into a window and use the zero crossing rate and short time energy to detect speech endpoints.)

能自动循迹 图像识别 语音识别 避障 红外光感应 过ETC等功能(Can automatic speech recognition obstacle avoidance tracking infrared induction ETC function)

采用矢量量化对语音信号量化，并与标量量化对比分析，绘制了语音信号的概率密度曲线(The use of vector quantization for speech signal quantization and scalar quantization and comparative analysis, rendering the speech signal probability density curve)

1.进行含噪语音信号的时频分析 2.设计合适滤波器进行去噪 3.进行去噪后信号的时频分析4.设计一个混响器（用四个梳状滤波器和两个全通滤波器（下图所示））来产生回声（通过一个均衡器(Reverberation)

Use of repeater transponder controls, WebForm controls

采样小波包分解语音信号，分解为3层，并求出分解系数(Speech signal using wavelet packet decomposition，decomposing 6 level，and attain decomposition coefficient)

语音的识别的前端技术，语音的基音周期检测(Front-end voice recognition technology, Voice Pitch Detection)

speech dereverberation using homomorphic processing