speech-signal-processing

c++语言开发，用于LPCC特征参数提取的，在语言识别系统中应用广泛。(c language, LPCC Features for feature extraction, in language recognition system widely used.)

观测实时模拟信号（语音）的频谱 用音频设备采集一段语音，将语音存为.wav格式。对wav文件作分段傅里叶变换分析。语音是分音节的，应把它分段分析，而且实际运用中的数字信号处理的FFT的点数是有限的，一般只能达到千点。用傅里叶反变换IFFT，从频域恢复信号。画出频谱图和语音波形图。 (Observing real-time analog signal (voice) of the spectrum collected with the audio devices section of voice, voice saved as. Wav format. Wav file of the sub-Fourier transform analysis to make. Speech is divided into syllables, it should be sub-analysis, and practical application of digital signal processing of the FFT points is limited, generally only reach 1000 points. Fourier inverse transform IFFT, to restore the signal from the frequency domain. Draw the spectrum map and voice waveform.)

mfcc特征提取法 以及svm训练 可以使用(MFCC feature extraction method and SVM training can be used)

C#的WinForm项目的dataGridView控件的CheckBox(The CheckBox of the dataGridView control for the WinForm project of the C #)

采用加权网络中节点强度和权重都是幂率分布的模型，计算两个矩阵之间的欧氏距离，实现了对10个数字音的识别。( Using weighted model nodes in the network strength and weight are power law distribution, Calculation of the Euclidean distance between the two matrices, To achieve the recognition of 10 digital sound.)

用NLMS实现对语音的回声的消除,共4个文件,语音原声,语音回声,NLMS的实现,回声路径(Using NLMS realize voice echo on the elimination of a total of four documents, voice, soundtrack, voice echo, NLMS realization echo path)

语音信号时频分析，包括语谱图，过零率，自编的自相关，以及最后用了三种方法（短时能量和过零率、谱熵法、Teager算子）进行端点检测，代码完整且测试通过(time and frequency domain analysis of speech signals,including spectrogram,rate of zerocrossing,autocorrelation.and three method of endpoint detection,especially the Teager )

利用mfcc特性进行语音识别，分类算法为svm。(Speech recognition using MFCC characteristics)

fastICA用于盲源分离，文件中有三个语音文件进行混合后再分离。(FastICA is used for blind source separation, in which three voice files are mixed and then separated.)

实现声源定位功能，利用两个麦克风的语音数据获得原始声源的位置!(Achieve sound localization function, using two microphones for voice access to the original source data location!)