ARMA modelling based on root cepstral deconvolution

S. Sarpal, E. Chilton
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引用次数: 5

Abstract

Cepstral deconvolution has been successfully applied to many diverse fields, such as speech and seismic analysis. Thus far, all cepstral modelling performance has been empirical, relying on the judgement of the designer. A novel method for measuring root cepstral ARMA modelling performance is proposed, by introducing a cost function applied directly to the root cepstral domain. It is, therefore, possible to demonstrate the optimised modelling of an ARMA process and show that its performance is superior to that of a FIR Wiener filter and linear prediction.
基于根倒谱反卷积的ARMA建模
倒谱反褶积已经成功地应用于许多不同的领域,如语音和地震分析。迄今为止,所有的倒谱建模性能都是经验性的,依赖于设计师的判断。通过引入直接应用于根倒谱域的代价函数,提出了一种测量根倒谱ARMA建模性能的新方法。因此,有可能证明ARMA过程的优化建模,并表明其性能优于FIR维纳滤波器和线性预测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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