基于小波的人类语音活动检测

Md. Shahadat Hossain, Md. Ariful Islam, M. Islam
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引用次数: 0

摘要

小波在当今的科学领域有着广泛的应用。目前利用小波通过MATLAB完成了不同类型的任务。例如,生物特征识别(指纹识别、语音识别、虹膜识别、人脸识别、模式识别和签名识别)、信号处理、人声活动检测等都是利用小波和小波变换来实现的。其中,我在这里讨论了“人类语音活动检测”。首先,使用一个好的耳机将人声作为MATLAB命令窗口的输入声音,持续几秒钟。然后,作为输入的声音给出一个图形表示,为将来的活动保存。然后利用MATLAB的小波工具箱对输入声音的图像进行分析。利用离散小波变换对图像进行分析。在分析过程中,采用Haar小波分解10级生成“10级小波”树。同时对原始信号进行重构。首次对同一人的六种不同的人声活动进行了分析。范数和信噪比(信噪比)被计算。信噪比数据以分贝(db.)为单位进行计数。同时计算了三种不同声音的码率。以这种方式,总共为不同的五个人做了18个不同的实验,除了第一个人之外,所有的人都做了三个实验。实验数值数据采用图形表示和直方图分析。在此过程中,完成了人类语音活动检测的整个实验。
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HUMAN VOICE ACTIVITY DETECTION USING WAVELET
Wavelet has wide range of use in the present scientific universe. At present using wavelet through MATLAB different types of tasks are done. For instance biometric recognition (fingerprint recognition, voice recognition, iris recognition, face recognition, pattern recognition and signature recognition), signal processing, human voice activity detection etc. are done using wavelet and wavelet transform. Among these here I have discussed about "Human Voice Activity Detection". At first a human voice is taken as the input sound to MATLAB command window using a good headphone for a few second. Then the sound taken as input give a graphical representation that is saved for future activities. After that using the wavelet toolbox of MATLAB the image of the input sound is taken for analyzing it. Using discrete wavelet transform the image is analyzed. During this analysis a "10 level wavelet" tree is generated by Haar wavelet with 10 decomposition level. At the same time the original signal is reconstructed. At the first time six different human voice activities of the same persons are analyzed. The Norm and the SNR (Signal to Noise Ratio) are counted. The data of the SNR are counted in decibel (db.) unit. Also the bit rates of the three different voice are counted. In this way total 18 different experiments are done for the different five persons where except the first person for all the person three experiments are dine.. The numerical data of the experiments are shown as graphical representation as well as in histogram analysis. In this process the whole experiments are done for the activity detection of human voice.
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