yuyinfenxi

在evmdm642平台下的一个语音采集-回放任务，用48K/秒的采样率采集立体声音频输入，用给定的FIR 滤波参数处理数据后再播放出来。注：内附个人整理的详细操作文档。(evmdm642 platform in a speech to the gathering-intervals task with 48 K/s sampling rate Acquisition stereo audio input, used to the FIR filter parameters for data processing later. Note : with a detailed personal finishing operations documentation.)

语音信号处理--降噪方法之小波分解法 MATLAB例程(Speech Signal Processing--A MATLAB Routine of Wavelet Decomposition Method for Noise Reduction)

语音增强算法综述，还可以，基本的谱减法，维纳滤波法都有(Overview of speech enhancement algorithm, can also basic spectral subtraction, Wiener filtering method has)

基于TDOA声源定位算法仿真--MATLAB仿真(Simulation of Sound Source Location Based on TDOA--MATLAB Simulation)

简单语音识别源码是一个调用讯飞语音识别SDK的例子源码是一个最纯净的Demo比较容易看懂。实现的是点击按钮开始语音监听，手机需要联网，2/3G的均可，识别后会以Toast的方式提示出来，识别率还是比较高的。之前自己是用别人的Demo总是不成功，是因为，讯飞的SDK想使用是需要APPID的，可能过太长时间久会注销，还有，未通过审核的APPID每天只能试用500次，如果太多人试用，就是达到上限导致无法使用。(A simple speech recognition source code is a call Ifly speech recognition SDK example source code is a demo of the purest easier to understand. The realization of the click button to start the voice listening, mobile phone need to networking, 2/3G can, after the recognition will be prompted by Toast way out, the recognition rate is still relatively high. Before he is with someone else s demo is not always successful, is because the Xunfei SDK to use is appid, may be too long for a long time will be canceled, and not through the audit appid every day can only be tried out 500 times, if too many people tried is reaching the limit to use.)

基于Matlab的织物疵点检测和识别系统(Fabric defect detection and identification systems based on Matlab)

HMM连续语音识别(Continuous Speech Recognition)

对 隐马尔科夫工具包的使用及安装进行说明，主要是在matlab下运用

熵值越大则每个符号包含的平均信息量越大。有研究发现，在有噪声的语音信号中，语音信号的熵和噪声信号的熵存在着较大的差异，对噪声信号来说在整个频带内分布相对平坦，熵值小，语音信号集中在某些特定频段内，熵值大。因此利用这个差异可以区分噪音段和语音段。(The greater the entropy is, the greater the average information of each symbol is. It is found that, in noisy speech signals, the entropy of speech signals and the entropy of noise signals are quite different. For noisy signals, the distribution is relatively flat in the whole frequency band, and the entropy value is small. The speech signal is concentrated in some specific frequency bands, and the entropy value is large. So the difference can be used to distinguish the noise segment and the speech segment.)

自相关函数的应用，进行基音检测，在MATLAB下进行仿真(Application of autocorrelation function for pitch detection, carried out in the MATLAB simulation)