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说明：  AMUSE，独立成分分析(ICA)算法之一，用于混合语音信号的盲分离(AMUSE, algorithm of independent component analysis, used in blind speech signal separation.)

为均衡带限信号所引起失真的横向或格型自适应均衡器(其中横向FIR系统长M=11), 系统输入是取值为±1的随机序列 ，其均值为零；参考信号 ；信道具有脉冲响应： 式中w用来控制信道的幅度失真(w = 2～4，例如，取w = 2.9,3.1,3.3,3.5等),而且信道受到均值为零、方差为 (例如，取 ，相当于信噪比为30dB)的高斯白噪声 的干扰。试比较基于下列五种算法自适应均衡器在不同信道失真、不同噪声干扰下的收敛情况(对应于每一种情况，在同一坐标下画出其学习曲线)： 横向/格-梯型结构LMS算法[1-4] 横向/格-梯型结构RLS算法[1-4] ()

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)

一个能实现对语音信号分帧的STFT和ISTFT的函数(Realize a sub-frame of the speech signal and the STFT of the function ISTFT)

语音增强算法，基于人耳听觉掩蔽的维纳滤波(Subspace speech enhancement algorithm based on human auditory masking Wiener filter)

采用广义旁瓣抵消（GSC）自适应波束形成方法实现时域和频域滤波，采用LMS自适应算法，最终实现语音增强。（文件中包含纯净语音及不同信噪比的带噪语音）(Generalized sidelobe canceller (GSC) adaptive beamforming method to achieve time-domain filtering using the LMS adaptive algorithm, and ultimately the speech enhancement)

语音信号的短时分析，主要包括：分帧、短时能量、短时平均幅度、短时过零率、短时自相关函数、短时幅度差、倒谱、复倒谱、lpc系数、lpc谱估计等 绝对保证质量，是保研后导师布置的一些基础程序(Short-time speech signal analysis, mainly including: sub-frame, short-time energy, short-term average, short-time zero-crossing rate, short-time auto-correlation function, short-term rate of poor cepstrum, complex cepstrum, lpc coefficients, lpc spectral estimation, such as an absolute guarantee that the quality of instructors is the security arrangement after the inquest some of the basis of procedures)

一个通过实际应用的“ 人工鱼群算法”，效果很好的。(Through the practical application of the " artificial fish-swarm algorithm," good effects.)

GMM说话人识别平台全套，从特征提取到模式识别，到识别率，一整套都有(GMM speaker recognition platform full, from feature extraction to pattern recognition, the recognition rate, set both)

块和子带自适应滤波器matlab源文件，内有详细说明及运行结果图(Block and Subband Adaptive Filters)