【作者】 王文良；

【导师】 郭继昌；

【作者基本信息】 天津大学 ， 信号与信息处理， 2007， 硕士

【摘要】 语音信号处理的实际应用中,不可避免地会受到来自周围环境噪声的影响从而导致语音质量的下降。语音增强的目的就是从带噪语音中提取尽可能纯净的原始语音。小波理论是一门新兴的时频分析技术,是分析类似于语音信号、地震信号等非平稳信号的有力工具。小波阈值去噪的主要思想是当含噪信号经小波变换由时域变换到小波域时,信号的小波系数相对集中在有限的区域内,而噪声的小波系数将分散到整个小波域。因此,即使输入信噪比比较低,信号变换后的小波系数也要大于噪声的小波系数。此时,可采用适当的阈值函数,在小波域内去除噪声系数,保留信号的系数,再由剩余的系数进行小波重构,即可恢复信号,达到去噪的目的。本文在小波阈值去噪方法的基础上,提出了一种新的基于人耳听觉特性的小波包变换语音增强算法。首先,我们将带噪语音进行Bark尺度小波包变换,从而很好地模拟人耳的听觉特性;接着通过分析小波语音增强中传统的软、硬阈值函数的缺点并结合大量的实验仿真,我们构造了一个新的阈值函数,实验结果表明新的阈值函数的增强效果比传统的阈值函数有了较大的改善。通过使用Matlab软件平台,我们对算法进行了实现。大量的仿真结果表明,我们提出的基于人耳听觉特性的小波包变换语音增强算法在主观和客观两方面都取得了较好的增强效果。更多还原

【Abstract】 In many speech processing applications, it is very common to find the degradation of the quality of speech caused by undesirable background noise. The goal of speech enhancement is to recover original speech signals from noisy observations.Wavelet theory is a newly developed time-frequency analysis technique and is especially of interest for the analysis of non-stationary signal such as speech, sonar seismic signal, etc. The main idea of wavelet thresholding lies in that when noising signal transforms from time domain to wavelet domain, the signal’s wavelet coefficients will spread to all area of wavelet domain. Although the energy of noise is bigger than the signal, its wavelet coefficients are smaller than the signal’s. So we can use thresholding function to cut off the coefficients of noise and use the rest of coefficients to reconstruct the denoising signal.This paper presents a new algorithm for speech enhancement based on wavelet thresholding method. First, we decompose the noisy speech by the Bark-scaled Wavelet Packet (BS-WPD) to simulate the human auditory characteristics. Then we propose a new thresholding function which has many advantages over soft and hard thresholdings put forward by D.L. Donoho and I.M. Johnstone. Simulation result proves that the proposed new thresholding function has a better improvement.At last, simulation of the algorithm based on Matlab software is implemented. A large amount of simulation results indicate that our new method based on Bark-scaled wavelet packet decomposition has a better performance in both objective and subjective aspects.更多还原