yuchuli

全面介绍语音合成的知识比较系统很适合语音学习(A comprehensive comparative introduction speech synthesis system is suitable for knowledge language learning)

量子粒子群算法应用。对三个水听器的位置定位，在假设水听器位置已知情况下，以及其他方法估计得到的时延，通过量子粒子群算法计算，验证量子粒子群算法的计算精度。(Quantum particle swarm optimization application. Position location of the three hydrophones in the hydrophone position assumed known case, and other methods for estimating time delay obtained by calculating the quantum PSO QPSO verify the accuracy of the algorithm.)

EVRC的语音编码算法研究及仿真,及二阶固定码本搜索算法(Speech Coding Algorithm and Simulation of EVRC)

实现已知训练序列的矢量量化器（LBG）算法。初始码书从训练序列每隔五个样本选取一组(Achieve a known training sequence of the vector quantizer (LBG) algorithm. The initial codebook from the training sequence every five samples to select a group of)

模式识别k聚类算法源代码，c语言内容描述。(Pattern Recognition Chapter )

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.)

基于FastICA相结合实现的语音信号的采集、随机混合，再通过盲分离将混合后的语音信号分离(Speech signal acquisition and random mixing based on FastICA, and then the mixed speech signal is separated by blind separation.)

实现在单信道语音信号处理中的降噪，用Spectral Subtraction算法实现的，内涵音频文件，代码中包括给纯净音频文件加噪声的部分。(Noise reduction in single-channel speech signal processing, using the Spectral Subtraction algorithm, the connotation of an audio file, the code in the package Including pure audio files and part of the noise.)

语音识别需要用到的函数，在你的程序中可直接调用工具箱中的函数(Needed by the voice recognition function in your program can directly call the functions in the toolbox)

端点检测算法采用现阶段比较精确的时频方差检测法，模式识别和模板匹配采用的是改进后的DTW算法（主要是限定了DTW的搜索路径，进一步精确了DTW的平行四边形的形状，进一步的减少匹配中的参数存贮量以及多余的搜索路径）。(Endpoint detection algorithm uses more precise at this stage variance time-frequency detection, pattern recognition and template matching is based on the improved DTW algorithm (DTW is mainly limited to the search path, and further precision of the DTW parallelogram shape, and further reduction of matching the parameters in the storage volume as well as the extra search path).)