有色背景噪声环境下语音增强系统的设计与实现

发布时间：2018-04-30 04:14

本文选题：语音增强 + 高阶累积量　；参考：《电子科技大学》2014年硕士论文

【摘要】：语音增强作为通信领域中一项重要技术手段,随着通信技术的快速发展,它在语音识别、降噪和语音编码等方面发挥着越来越重要的作用,成为了近30年中语音处理领域的热点话题。它以提高语言信号信噪比,改善语音质量为目标,进而提高语音信号的舒适度与可懂度,有着十分重要的实用意义。语音信号数字模型是语音信号处理的基础,模型的准确性将直接影响到语音信号的后续处理。本文将建立一个全极点模型,该模型结构简单易于实现。语言增强算法的重点是对语音信号模型和噪声做参数估计,模型参数是卡尔曼滤波算法基础。噪声参数估计主要是通过VAD算法对语音作有效检测,在信号无声段通过LPC自相关法直接估计。接着使用一些常用的参数估计算法,如极大似然函数法、Burg算法等,进行仿真实验。但低信噪比下,这些方法将误差增大,变的不稳定。所以本文在此基础上应用了基于高阶累积量的参数估计算法,根据其特性可知纯净语音信号与带噪信号的高阶累积量相等。根据这一事实,我们便可以把AR模型通过高阶累积量表示,即MYW方程。而MYW方程的求解方法主要有辅助变量法、LMS算法。通过改进LMS算法,本文提出了共轭梯度算法,通过实验证明,效果与LMS算法类似,但可以简化运算。最后通过卡尔曼滤波,实现语音增强。通过仿真实验结果分析,高阶累积量结合共轭梯度算法可以很好的实现语言增强,不仅提高了增强算法的适用范围同时简化了运算。最后,为了验证算法的可行性、实时性,本文设计了硬件平台;利用芯片TMS320VC5402作处理器,TLV320AIC23B采集音频信号,STC89LE58RD+作控制器,这样不仅节约了成本,同时提高了效率。
[Abstract]:As an important technical means in the field of communication, speech enhancement plays a more and more important role in speech recognition, noise reduction and speech coding with the rapid development of communication technology. It has become a hot topic in the field of speech processing in the past 30 years. It aims at improving the signal-to-noise ratio (SNR) and speech quality of speech signals, and then improves the comfort and intelligibility of speech signals, which is of great practical significance. Speech signal digital model is the basis of speech signal processing, the accuracy of the model will directly affect the subsequent processing of speech signal. In this paper, a full pole model is established, which is simple and easy to implement. The emphasis of speech enhancement algorithm is to estimate the parameters of speech signal model and noise, and the model parameters are the basis of Kalman filter algorithm. Noise parameter estimation mainly uses VAD algorithm to detect speech effectively, and LPC autocorrelation method is used to estimate the noise parameter directly in the silent segment of the signal. Then some commonly used parameter estimation algorithms, such as the maximum likelihood function method and Burg algorithm, are used to carry out simulation experiments. However, at low SNR, these methods will increase the error and become unstable. In this paper, a parameter estimation algorithm based on high order cumulant is applied. According to its characteristics, the high order cumulant of pure speech signal and noisy signal is equal. According to this fact, we can express AR model by higher order cumulant, that is, MYW equation. The main methods for solving MYW equation are the auxiliary variable method and the LMS algorithm. The conjugate gradient algorithm is proposed by improving the LMS algorithm. It is proved by experiments that the effect is similar to that of the LMS algorithm, but the operation can be simplified. Finally, speech enhancement is realized by Kalman filter. The simulation results show that high order cumulant combined with conjugate gradient algorithm can achieve language enhancement, which not only improves the application range of the enhancement algorithm, but also simplifies the operation. Finally, in order to verify the feasibility and real time of the algorithm, this paper designs a hardware platform and uses chip TMS320VC5402 as the controller to collect audio signal from TLV320AIC23B, which not only saves the cost but also improves the efficiency.
【学位授予单位】：电子科技大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TN912.35

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