yuyin

一个基于小波变换的图像去噪声程序 仿真软件采用的是matlab(a Wavelet-Based Image noise process simulation software using the Matlab)

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.)

利用DTW模版匹配算法实现0~9十个数字的识别。(use DTW template matching algorithm 0-9 10-digit identification.)

基于动态时间规整与隐马尔科夫模型的自动语音识别系统(Automatic speech recognition system based on dynamic time warping and hidden Markov model)

本实例程序是基于matlab的语音识别程序，通过提取传统的MFCC特征集，采用当前流行的SVM分类器，作为对比实验是一个不错的选择。(This example program is popular SVM classifier matlab speech recognition program to extract MFCC feature set, as a comparative experiment is a good choice. )

这是一个很好的频域分块自适应滤波的程序，应用于回声消除上，并与NLMS自适应滤波做了运行速度的比较,FLMS比NLMS快几十倍。(This is a very good frequency-domain block adaptive filtering procedure applied to echo cancellation, and comparison with the NLMS adaptive filter for speed, FLMS several times faster than the NLMS.)

很好的一本关于语音信号处理的书，而且是中文版的，比较好用(A good book about the processing of speech signal)

微软研究院的说话人识别工具包，包括GMM-UBM、I-Vector。其中demo_gmm_ubm_artificial.m和demo_ivector_plda_artificial.m为生成模拟特征参数进行训练与识别的教学示例，十分适合初学者学习说话人识别基础算法。具体使用方法请看内部文档。(Microsoft Research s speaker recognition toolkit, including GMM-UBM, I-Vector. Demo_gmm_ubm_artificial.m and demo_ivector_plda_artificial.m which generates an analog characteristic parameters for example teaching training and recognition, very suitable for beginners to learn the basic algorithm for speaker recognition. See the specific use of internal documents.)

VAD去噪算法，可以有效的过滤静音和无效的语音，同时消除了一些背景噪声。(VAD denoising algorithm, can effectively filter invalid mute and voice, while eliminating some background noise.)

本资料涵盖了几乎所有的语音增强方面的方法，主要有谱减法，听觉掩蔽，最小均方误差，维纳滤波以及一些非主流的方法，这些对于研究语音增强的人来说是很有帮助的(The data cover almost all aspects of speech enhancement methods, the main spectral subtraction, auditory masking, minimum mean square error, Wiener filtering as well as some non-mainstream approach, which for the study of speech enhancement is helpful for people who)