基于Hilbert-Huang的语音信号去噪算法研究
发布时间：2019-05-08 21:37
【摘要】：语音信号不仅具有蕴含信息量最多的特性,而且还是语音信号处理领域的重要组成部分。现实生活中语音信号是非平稳的时变随机信号,都会受到噪声的污染,因此必须进行语音信号去噪处理。傅立叶变换、短时傅立叶变换、小波变换对非平稳随机信号处理的效果不好,Hilbert-Huang变换具有的多分辨率和高度自适应的特性使得它非常适合处理非平稳非线性的时变随机信号,本论文提出了基于Hilbert-Huang的语音信号去噪算法研究。首先,介绍了语音和噪声的基本特性,傅立叶变换、短时傅立叶变换、小波变换的基本理论,常见的语音信号去噪方法的去噪原理和常用的语音信号的语音质量的评价标准。其次,详细阐述了Hilbert-Huang变换的基本理论和算法实现过程,分析了Hilbert-Huang变换的EMD分解和Hilbert变换的解析过程,并仿真实现了三种不同的信号的Hilbert-Huang变换。再次,根据语音信号的短时平稳性、三次样条插值法拟合信号曲线的平均包络的过程中产生的欠冲和过冲的现象和Hilbert-Huang变换过程中噪声和有用信号在IMF分量中能量分布的不同选取筛选分界点问题,提出了改进的Hilbert-Huang语音信号去噪算法。最后,利用matlab进行仿真对比,分别采用小波变换、Hilbert-Huang变换和本文改进的Hilbert-Huang变换对加噪后语音信号进行处理,仿真结果显示:本文改进的Hilbert-Huang算法具有更好的去噪效果,去噪后的语音信号不仅有较好的时频波形,而且有较高的信噪比。
[Abstract]:Speech signal not only contains the most information, but also is an important part of speech signal processing. In real life, speech signal is non-stationary time-varying random signal, which will be polluted by noise. Therefore, speech signal denoising must be carried out. The effect of Fourier transform, short time Fourier transform and wavelet transform on non-stationary random signal processing is not good. Because of its multi-resolution and highly adaptive properties, Hilbert-Huang transform is very suitable to deal with non-stationary and nonlinear time-varying random signals. In this paper, a speech signal denoising algorithm based on Hilbert-Huang is proposed. Firstly, the basic characteristics of speech and noise, the basic theory of Fourier transform, short-time Fourier transform, wavelet transform, the de-noising principle of common speech signal de-noising methods and the evaluation standard of speech quality of common speech signal are introduced. Secondly, the basic theory and algorithm of Hilbert-Huang transform are described in detail, the EMD decomposition of Hilbert-Huang transform and the analytical process of Hilbert transform are analyzed, and the Hilbert-Huang transform of three different signals is simulated. Again, based on the short-term stationarity of the voice signal, In the process of fitting the mean envelope of the signal curve by cubic spline interpolation, the undershoot and overshoot phenomena produced in the process of fitting the mean envelope of the signal curve and the different selection and selection of the boundary points for the energy distribution of the noise and useful signals in the IMF component during the Hilbert-Huang transformation are also discussed. An improved Hilbert-Huang speech signal denoising algorithm is proposed. Finally, matlab is used to simulate and compare, and wavelet transform, Hilbert-Huang transform and the improved Hilbert-Huang transform are used to process the noisy speech signal, and the wavelet transform, the Hilbert-Huang transform and the improved Hilbert-Huang transform are used to process the noisy speech signal. The simulation results show that the improved Hilbert-Huang algorithm has better denoising effect. The de-noised speech signal not only has better time-frequency waveform, but also has a higher signal-to-noise ratio.
【学位授予单位】：长春理工大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TN912.3
，
本文编号：2472239
[Abstract]:Speech signal not only contains the most information, but also is an important part of speech signal processing. In real life, speech signal is non-stationary time-varying random signal, which will be polluted by noise. Therefore, speech signal denoising must be carried out. The effect of Fourier transform, short time Fourier transform and wavelet transform on non-stationary random signal processing is not good. Because of its multi-resolution and highly adaptive properties, Hilbert-Huang transform is very suitable to deal with non-stationary and nonlinear time-varying random signals. In this paper, a speech signal denoising algorithm based on Hilbert-Huang is proposed. Firstly, the basic characteristics of speech and noise, the basic theory of Fourier transform, short-time Fourier transform, wavelet transform, the de-noising principle of common speech signal de-noising methods and the evaluation standard of speech quality of common speech signal are introduced. Secondly, the basic theory and algorithm of Hilbert-Huang transform are described in detail, the EMD decomposition of Hilbert-Huang transform and the analytical process of Hilbert transform are analyzed, and the Hilbert-Huang transform of three different signals is simulated. Again, based on the short-term stationarity of the voice signal, In the process of fitting the mean envelope of the signal curve by cubic spline interpolation, the undershoot and overshoot phenomena produced in the process of fitting the mean envelope of the signal curve and the different selection and selection of the boundary points for the energy distribution of the noise and useful signals in the IMF component during the Hilbert-Huang transformation are also discussed. An improved Hilbert-Huang speech signal denoising algorithm is proposed. Finally, matlab is used to simulate and compare, and wavelet transform, Hilbert-Huang transform and the improved Hilbert-Huang transform are used to process the noisy speech signal, and the wavelet transform, the Hilbert-Huang transform and the improved Hilbert-Huang transform are used to process the noisy speech signal. The simulation results show that the improved Hilbert-Huang algorithm has better denoising effect. The de-noised speech signal not only has better time-frequency waveform, but also has a higher signal-to-noise ratio.
【学位授予单位】：长春理工大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TN912.3
，
本文编号：2472239
本文链接：https://www.wllwen.com/kejilunwen/xinxigongchenglunwen/2472239.html
