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小波、适配波形与去噪

Wavelets, adapted waveforms and de-noising.

作者信息

Coifman R R, Wickerhauser M V

机构信息

Department of Mathematics, Yale University, New Haven, CT 06520, USA.

出版信息

Electroencephalogr Clin Neurophysiol Suppl. 1996;45:57-78.

PMID:8930516
Abstract

This is a short summary of a talk given at the Frontier Science in EEG Symposium, Continuous Waveform Analysis, held on 9 October 1993 in New Orleans. We describe some new libraries of waveforms well-adapted to various numerical analysis and signal processing tasks. The main point is that by expanding a signal in a library of waveforms which are well-localized in both time and frequency, one can achieve both understanding of structure and efficiency in computation. We briefly cover the properties of the new "wavelet packet" and "localized trigonometric" libraries. The main focus will be applications of such libraries to the analysis of complicated transient signals: a feature extraction and data compression algorithm for speech signals which uses best-adapted time and frequency decompositions, and an adapted waveform analysis algorithm for removing fish noises from hydrophone recordings. These signals share many of the same properties as EEG traces, but with distinct features that are easier to characterize and detect.

摘要

这是1993年10月9日在新奥尔良举行的脑电图前沿科学研讨会“连续波形分析”上所做演讲的简短摘要。我们描述了一些非常适合各种数值分析和信号处理任务的新波形库。关键在于,通过在时间和频率上都具有良好局部化特性的波形库中展开信号,既能理解信号结构,又能提高计算效率。我们简要介绍了新的“小波包”和“局部三角”库的特性。主要重点将是此类库在复杂瞬态信号分析中的应用:一种用于语音信号的特征提取和数据压缩算法,该算法使用了最佳适配的时间和频率分解;以及一种用于去除水听器记录中的鱼类噪声的适配波形分析算法。这些信号与脑电图迹线具有许多相同的特性,但具有更易于表征和检测的独特特征。

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