negentropy

（1）理解矢量量化的基本原理。 （2）掌握矢量量化器设计的LBG方法。 （3）运用LBG方法获取语音短时平均能量码书((1) to understand the basic principles of vector quantization. (2) master the vector quantizer design LBG method. (3) Using the LBG method to obtain the speech short time average energy code book)

Praat4610 语音合成及分析源代码(Praat4610 speech synthesis and analysis of source code)

核磁共振（NMR）技术探测地下水是目前唯一的直接找水的地球物理方法。与传统的地球物理方法相比具有高分辨力，高效率，信息量丰富和解唯一性等优点。利用核磁共振地下水探测系统可以高效率地进行区域水文地质调查，确定找水远景区，圈定地下水在三维空间内的分布，进而可靠地选定水井位置等。但是由于水中氢核产生的核磁共振信号的幅度小（纳伏级），要求探测系统的灵敏度高，将引入大量的自然和人为噪声，导致采集信号的信噪比低，解释结果不清楚。 本文用LMS自适应算法提高MRS信号的信噪比，用Hilbert变换提取MRS信号的包络信号，用线性拟合提取水文地质参数并通过仿真实验结果，验证了本文提出的数据处理方法的有效性和准确性。 (Nuclear magnetic resonance (NMR) technique to the detection of direct water division is the only geophysical method to find water. Compared with conventional high-resolution geophysical methods, efficient, informative unique advantages of reconciliation. Groundwater exploration using nuclear magnetic resonance system can efficiently carry out the regional hydrogeological investigation to determine the prospective areas to find water, delineation of ground water distribution in three-dimensional space, and then reliably the location of the selected wells. However, because the water proton NMR signal amplitude generated by small (Na V level), requiring high sensitivity detection systems, will introduce a large number of natural and man-made noise, resulting in low signal to noise ratio acquisition, interpretation of results is not clear. In this paper, LMS adaptive algorithm improves the MRS signal to noise ratio, with the Hilbert transform to extract the signal envelope MRS signal ext)

本文主要研究普通计算环境下语音自动识别(ASR)技术的软件实时实现，基于语音识别的基本原理和过程。在VC环境下建立了一套语音识别系统及其演示软件。实验表明，该系统对特定人情况有较高的识别率和一定的实时性(In this paper, under the computing environment on general automatic speech recognition (ASR) technology to achieve real-time software, speech recognition based on the basic principles and processes. VC environment in the establishment of a voice recognition system and its presentation software. The experiments show that the systems are in particular have a high recognition rate and a certain degree of real-time)

实现语音的分割和识别，语音分割通过短时能量和过零率，语音识别通过dtw算法。(audio cut and reconige)

适用于对各种语音信号的分帧，将语音信号分段处理(Applicable in a variety of speech signal sub-frame, the speech signal Break Processing)

语音库开发工具源码，例程程序创建机读SAPI.SpVoice对象开发语音库。(Speech development tools source , routine program to create machine-readable SAPI.SpVoice speech object development .)

对一维语音信号，用正交匹配追踪的方法进行重构，首先对其进行了分帧，然后再一帧一帧的重构，最后计算了平均帧重构误差(One-dimensional speech signal reconstruction, orthogonal matching pursuit, the first of its sub-frame, and then again a reconstruction of a final calculation of the average frame reconstruction error)

语音唤醒就是在设备待机状态下，用户说出特定的语音指令（唤醒词）使设备进入工作状态或完成某一操作。设备为了实现语音唤醒功能，就需要设备实时监听，也就是实时的录音并分析有没有唤醒词来唤醒设备。评价一个语音唤醒系统的标准有三个：唤醒正确率、误报率、功耗。一般情况下，唤醒正确率越高 ，误报率也越高。好的系统就需要唤醒率高，误报率低，功耗低。(Speech wakeup is when the device is in standby mode, the user speaks a particular voice command (wake up word), so that the device enters the work state or completes an operation. In order to realize the function of speech wakeup, the device needs real-time monitoring, that is, real-time recording and analyzing whether or not wake words are used to wake up the equipment. There are three criteria for evaluating a voice wakeup system: wakeup accuracy, false positive rate, and power consumption. Generally, the higher the correct rate of arousal, the higher the false positive rate. A good system requires high wake-up rate, low false positive rate and low power consumption.)

这 里主要对LMS算法及一些改进的LMS算法（NLMS算法、变步长LMS算法、变换域LMS算法）之间的不同点进行了比较，，在传统的LMS算法的基础上发 展了LMS算法的应用。另一方面又从RLS算法的分析析中对其与LMS算法的不同特性进行了比较。 (Here the main difference between the LMS algorithm and improved LMS algorithm (NLMS algorithm, variable step size LMS algorithm, the transform domain LMS algorithm) comparison, the traditional LMS algorithm based on the development of the application of the LMS algorithm . On the other hand and from its different characteristics of the LMS algorithm of the analytical analysis of the RLS algorithm.)