本课程从数字信号处理的基本原理回顾入手，系统地梳理数字信号处理中的概念、理论和方法，并结合实践中的具体问题深入讲解运用这些理论和方法解决问题的途径。第二部分是通过引入“自适应”的概念，全面讲述自适应信号处理的理论方法和研究思路，对若干常见的自适应系统进行深入的分析；第三部分概要讲述语音信号处理中的基本方法，着重讲述隐马尔科夫模型的概念、原理和方法，以及在语音识别中的应用；第四部分讲述分形于混沌的基本概念、典型分形信号处理的方法，以及分形理论在信号和图像压缩中的应用；第五部分讲述独立主元分析的理论方法和基本原理；第六部分结合生物信息学，讲述信号识别的基本方法和在生物信息处理技术中的应用。

In this lesson, the principals of digital signal processing are first reviewed, followed by discussing the basic concepts,theory and methodologies of digital signal processing in a systematic way and developing their solutions into real-world problems. In the seond part, the concept of adaptiveness is introduced and extended with a comprshensive discussion of the methodologies and research insights of adaptive signal processing and several canonical adapative systems. The third part briefly reviews some basical methods for signal processing, and focused on the concepts, principals and methodologies of Markov model as well as its application in the speech recognition. While the basic concepts of fractal and chaos, classical fractal signal processing methods and the application of the fractal in the signal and image compression are described in the fourth part, the fifth part presents the theory and principals of principal component analysis. Finally, the methodologies and applications relating to signal recognition in the bioinfomatics realm are discussed in the sixth part.