{"title":"Time frequency analysis: A sparse S transform approach","authors":"Kashyap Patel, N. Kurian, N. George","doi":"10.1109/ISPACS.2016.7824713","DOIUrl":null,"url":null,"abstract":"S transform, which is a powerful time frequency analysis method, has found applications in diverse areas of science and technology. The computational load offered by the S transform increases with increase in the length of the time series which is analysed. In an endeavour to reduce the computational load for time series which is sparse in the frequency domain, a new method for S transform computation is proposed in this paper. The new method uses an efficient search method to identify significant frequency indices and computes the S transform only at the selected frequency indices, thus reducing the computational burden. A simulation study has been carried out to test the efficiency of the proposed method for analytic and real-life signals. The proposed scheme has been shown to provide good signal reconstruction accuracy at a reduced computational load.","PeriodicalId":131543,"journal":{"name":"2016 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISPACS.2016.7824713","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
S transform, which is a powerful time frequency analysis method, has found applications in diverse areas of science and technology. The computational load offered by the S transform increases with increase in the length of the time series which is analysed. In an endeavour to reduce the computational load for time series which is sparse in the frequency domain, a new method for S transform computation is proposed in this paper. The new method uses an efficient search method to identify significant frequency indices and computes the S transform only at the selected frequency indices, thus reducing the computational burden. A simulation study has been carried out to test the efficiency of the proposed method for analytic and real-life signals. The proposed scheme has been shown to provide good signal reconstruction accuracy at a reduced computational load.