首页> 外文会议>International Symposium on Intelligent Signal Processing and Communication Systems >Blind signal separation for heart sound and lung sound from auscultatory sound based on the high order statistics
【24h】

Blind signal separation for heart sound and lung sound from auscultatory sound based on the high order statistics

机译:基于高阶统计,从综科声音的心声和肺部声音盲信号分离

获取原文

摘要

The auscultatory sounds are mixed heart sound, lung sound and other noises. If an auscultatory sound can be separated to a heart sound and a lung sound, it is possible that it may be useful for automatic diagnosis disease of the heart and lungs. However, a conventional method using the blind signal separation based on an ICA has lower separation precision. In the conventional method, a nonlinear function based on the separated signals is used in the separation algorithm. It is supposed to be an appropriate function in terms of separation precision corresponding to the probability distribution of source signals. However, because the conventional method has not been given an appropriate nonlinear function, it is a cause of lower separation precision. In this paper, we estimate a probability distribution of the source signals from the high order statistics of the separated signals and propose a better method which uses an appropriate nonlinear function in term of separation precision. Moreover, we evaluate a proposed algorithm to improve separation precision.
机译:听起来的声音是混合的心声,肺部声音和其他噪音。如果智慧声音可以与心脏声音分开和肺部声音,则可能对心脏和肺的自动诊断疾病有可能。然而,使用基于ICA的盲信号分离的传统方法具有较低的分离精度。在传统方法中,在分离算法中使用基于分离信号的非线性函数。应该在对应于源信号的概率分布的分离精度方面是适当的功能。然而,因为常规方法尚未被赋予适当的非线性函数,所以它是分离精度较低的原因。在本文中,我们估计来自分离信号的高阶统计的源信号的概率分布,并提出了一种更好的方法,该方法在分离精度期间使用适当的非线性函数。此外,我们评估了一种提高分离精度的提出算法。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号