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JAVA的声音处理方法 (转自伊氏女人)-----淘特网
- 数字音频格式有很多种，其质量与采样频率和采样精度两个参数有关。频率的表示单位为赫兹〔Hz〕，它表示每秒采样次数。采样频率越高，音质就越好。采样精度为每次采样所存储的数据数量，它决定每个数字信号所能够表示的离散振幅的数量。存储每个样本的数据越多，音质就越好。但是高品质的声音需要占用大量的内存和磁盘空间。考虑到网络带宽，在Internet连接上传输就需要花费很长的时间。对于Applet来说，保证声音文件的最小化是极为重要的。-digital audio format there are many,
SpeechSource
- speech file to be analyzed Format : binary file 16 bit-samples 240 samples per frame -speech file to be analyzed Format : 16 bit binary file-samples of 240 samples per fram e
FormantTracker(matlab)
- 检测语音信号的共振峰 ％ gender = gender_detector(X,Fs) ％ ％ This function will use a pitch detection algorithm to decide if the speaker is MALE(0) or FEMALE (1). ％ It is designed to work with short speech samples (up to or greater than 50 ms). The funct
voiceandmatlab
- 对语音进行的时频分析,语音识别技术作为信息社会朝着智能化和自动化方向发展的关键技术之一，具有重要的研究意义和实用价值。经过近五十年的艰苦探索和研究，语音识别技术研究获得了极大的发展，其中有些比较成熟的技术已经逐步应用于日常生活中。但总体来说，语音识别在研究和实用化方面的难度还比较大。本文概括介绍了语音识别技术的全貌，所采用的关键技术、具体应用以及当前所面临的困难,并对单个语音样本在MATLAB上进行了仿真分析。-right voice for the time-frequency analysi
HTK-samples-3.4
- 隐马项目\\HTK-samples-3.4.zip 这是一个以HMM为基础的语音训练模形源码从中可以了解HMM如何做参数撷取及如何辨识 -Hidden projects \\ HTK-samples - 3.4.zip this is a basis for the HMM Voice training pattern source from which one can understand how HMM parameters extracted and How to Tell
HTK-samples-3.4-alpha.tar.gz
- 语音识别工具HTK最新版，由英国剑桥大学开发,HTK Speech Recognition tools latest version, developed by the University of Cambridge
matlab_signal_process
- 语言处理过程中的端点检测，频谱分析，加重及去噪增强处理的MATLAB源程序和结果，语音文件改成自己的即可（采样点匹配）。-Language processing, endpoint detection, spectrum analysis, emphasis and de-noising and enhancement results of the MATLAB source code, voice files can be changed to your own (matched sample
wave
- C语言编写。对语音（.wav文件）做读取，写入操作，并可对文件进行采样抽取和内插-C language. Voice (. Wav files) to do read, write, and samples of documents collected and interpolation
htk
- 已经编译好了的HTK工具包，具有语音识别等功能，代码齐全，同时具有参考手册-Has been compiled of the HTK toolkit, with features such as voice recognition, code complete, at the same time with reference manual
lpc
- 功能：计算lpc系数由lpc得到lpcc，对模板参数和样本参数进行dtw比较 -Function: lpc coefficient calculated by the lpc be lpcc, samples of the template parameters and parameters of Comparative dtw
silence_remove
- This code is used to remove silence from a voice signal so that the recognition in voice recognition will give good result. The method is Scale on threshold applied to the envelope for detecting scilence periods. The actual threshold is computed by m
generatesample
- 生成《matlab扩展编程》中对于HMM训练的samples.mat程序，自己编写-Generation ‘matlab programming expansion’ in the HMM training samples.mat procedures, written by myself
VoiceConferencing
- Voice conferencing in .NET made easy , with samples
HMM
- ：为了使应力变异在顽健语音识别系统中能够达到较好的识别效果，研究了基于隐马 尔可夫模型(HMM)的自适应技术，提出了将最大后验概率(MAP)和最大似然回归方法(MLLR)用 于应力变异语音的自适应中。实验结果表明，与基本系统相比，两种方法均有效地提高系统识别 率。以SD为初始模型的最大后验概率方法在150个训练样本时识别效果最好，可以达到90．4％ 。-: In order to stress variation in the robustness of speech recogni
HTK-samples-3.4
- HTK3.4语音识别的实用例子-Practical examples HTK3.4
Research_on_DTW-based_speech_recognition_for_voice
- 研究了将语音识别中的 ( 动态时间规整) 算法用于声纹鉴别的技术。通过引入 DTW Dynamic Time Warping, , 由所给的有限个样本建立最大相似于样本点的样本域, 计算被测样本的相似度。该算法提高了语 “样本域”的概念 音鉴别( 区分不同发音者) 的效率。有限人数的实验结果显示该算法辨伪率为 ( 人次) , 识别率 (98.75 400 81~93 80 人次) 。-Will be studied in speech recognition (dynamic
Speech_LPC
- This GUI is used to analyze the speech signal at the selected region of 256 samples. All the calculation is based on the sampling of 8 KHz. First 3 formants of the selected block of samples are derived from the LPC-8 coefficients. -This GUI is used t
trakwindowgenerator
- It is simple program to create windowing parameters used to prepare samples in next steps of my master thesis program. Function used to generate parameters are taken from wikipedia ttp://en.wikipedia.org/wiki/Window_function
pattenrecongition
- 通过用最小距离分类判别方法，用MATLAB程序找出最小距离分类判别时的识别界面，从而进行识别已知的两类训练样本，并分析其识别错误率。-By using minimum distance classifier discriminant method, using MATLAB program to find the minimum distance classifier recognition interface when the judge, which is known to identify
train-samples
- training samples for training in speech recognition