摘要
摘要:现有的盲源分离算法不适合于数据的实时处理,并且算法性能依赖于步长的选择。提出一种基于信息最大化的自适应变步长盲源分离算法,采用基于估计函数的变步长算法,降低了盲源分离算法性能对步长的依赖性,并且采用自适应处理形式,适合数据的实时处理。最后将其应用于声音信号的盲分离,在选择小的步长参数的情况下,原有算法和文中新算法都取得了良好的分离效果;在选择较大的步长参数的情况下,新算法优于传统算法。
The behavior of the classic algorithms for blind source separation （BSS）is not competent to real-time process and depends greatly on a fixed step size. It is difficult to choose a pretty step size. In this paper, the algorithm of blind source separation with a variable step size based on estimation function is proposed to improve the performance of the traditional algorithms. The step-size of the algorithm is adjusted to fit real-time process. Then, the two algorithms are used in blind source separation of sound signal, when choosing the step-size with little value, both two algorithms can work well,while in the situation of step-size with large value, the performance of the new algorithm proposed here is excellent than that of the old one.
出处
《电子测量技术》
2007年第6期15-18,共4页
Electronic Measurement Technology
关键词
自适应变步长算法
盲源分离
信息最大化
adaptive variable step-size algorithm
blind source separation
information maximization
