{"title":"An overview of multiple-window and quadratic-inverse spectrum estimation methods","authors":"D. Thomson","doi":"10.1109/ICASSP.1994.389911","DOIUrl":null,"url":null,"abstract":"Presents examples, history, and a brief review of the theory of multiple-window and quadratic-inverse spectrum estimation methods for mixed harmonizable processes. In addition to the standard uses of making consistent non-parametric auto- and cross-spectrum estimates with jackknife confidence intervals and estimating periodic components in coloured noise, quadratic-inverse theory gives a time-frequency decomposition for stochastic processes. This leads to new estimates of both common and less-familiar functions such as the \"time-derivative\" of a spectrum.<<ETX>>","PeriodicalId":290798,"journal":{"name":"Proceedings of ICASSP '94. IEEE International Conference on Acoustics, Speech and Signal Processing","volume":"45 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1994-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"29","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of ICASSP '94. IEEE International Conference on Acoustics, Speech and Signal Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICASSP.1994.389911","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 29
Abstract
Presents examples, history, and a brief review of the theory of multiple-window and quadratic-inverse spectrum estimation methods for mixed harmonizable processes. In addition to the standard uses of making consistent non-parametric auto- and cross-spectrum estimates with jackknife confidence intervals and estimating periodic components in coloured noise, quadratic-inverse theory gives a time-frequency decomposition for stochastic processes. This leads to new estimates of both common and less-familiar functions such as the "time-derivative" of a spectrum.<>