Abdul_Syafiq_Abdull_Sukor

语音信号去噪，在MATLAB开发环境下，对含噪声的语音信号进行卡尔曼滤波，实现语音增强的功能。(The voice signal denoising, the Kalman filter, the noisy speech signal in the MATLAB development environment Wave, speech enhancement.)

不错的盲源分离工具箱，实现对声源的分离,包含多个子程序(a nice toolbox of blind source separation used to separate multiple sources)

实现语音识别，使用HTK工具。README描述了具体的训练过程。(Voice recognition, using HTK tools. README describes the specific training process.)

ARMA模型建立程序，给出自相关序列，指定ARMA模型的P阶，Q阶参数，得出相应的参数值 function [bq ,ap]=armahat(rx,p,q)(ARMA model for the establishment of procedures, to come from related sequences, designated the P-order ARMA model, Q-order parameters, the corresponding parameter values derived function [bq, ap] = armahat (rx, p, q))

本系统是基于矢量量化技术的说话人识别系统。可根据语音库以及实时录音识别且具有图形用户界面。压缩包内含有源码、语音信息库音频及演示视频等。(The system is based on vector quantization speaker recognition system. Identifiable and has a graphical user interface based on speech and real-time recording. Within the compressed package contains the source code, voice information and presentation of audio and video.)

实现mffc求解 并且可利用hmm进行孤立词识别(Solving mffc realize and can be used for isolated word recognition hmm)

3、语音特征提取与分类 首先, 待识别语音转化为电信号后输入识别系统, 经过预处理后用数学方法提取语音特征信号, 提取出的语音特征信号可以看成该段语音的模式。(3, the voice feature extraction and classification First, to be recognized voice into electrical signals input recognition system, after pretreatment with a mathematical method to extract speech characteristic signal extracted speech characteristic signal can be seen as the voice segment model.)

基于熵的端点检测算法，比传统的端点检测能更精确的检测出端点(endpoint detection algorithm than the traditional endpoint detection more accurate detection of Endpoint)

智能天线波束形成的LMS、SMI、RLS算法的程序(Smart antenna beamforming of LMS, SMI, RLS algorithm procedure)

本文介绍MVDR算法实现及其改进 用于多麦克风语音增强(The minimum variance distortionless response (MVDR), originally developed by Capon for frequency-wavenumber analysis, is a very well established method in array process-ing. It is also used in spectral estimation. The aim of this paper is to show how the MVDR method can be used to es-timate the magnitude squared coherence (MSC) function, which is very useful in so many applications but so few methods exist to estimate it)