{"title":"离散旋转Gabor变换","authors":"A. Akan, L. Chaparro","doi":"10.1109/TFSA.1996.547208","DOIUrl":null,"url":null,"abstract":"In this paper, we present a discrete rotational Gabor transform and apply it to signal representation on a non-rectangular time-frequency plane tiling. We use a modified version of the recently proposed discrete rotational Fourier transform. The rotational Gabor transform decomposes a signal using as basis functions scaled, translated and modulated by linear chirps windows. As a result, the time-varying frequency content of a signal is represented better than with sinusoidal modulated expansions. For a multi-component signal, the Gabor coefficients are obtained by combining different tilings to maximize a local energy concentration measure. This permits us to achieve a highly localized time-frequency signal representation. Examples are given to illustrate the transformation.","PeriodicalId":415923,"journal":{"name":"Proceedings of Third International Symposium on Time-Frequency and Time-Scale Analysis (TFTS-96)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1996-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Discrete rotational Gabor transform\",\"authors\":\"A. Akan, L. Chaparro\",\"doi\":\"10.1109/TFSA.1996.547208\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we present a discrete rotational Gabor transform and apply it to signal representation on a non-rectangular time-frequency plane tiling. We use a modified version of the recently proposed discrete rotational Fourier transform. The rotational Gabor transform decomposes a signal using as basis functions scaled, translated and modulated by linear chirps windows. As a result, the time-varying frequency content of a signal is represented better than with sinusoidal modulated expansions. For a multi-component signal, the Gabor coefficients are obtained by combining different tilings to maximize a local energy concentration measure. This permits us to achieve a highly localized time-frequency signal representation. Examples are given to illustrate the transformation.\",\"PeriodicalId\":415923,\"journal\":{\"name\":\"Proceedings of Third International Symposium on Time-Frequency and Time-Scale Analysis (TFTS-96)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1996-06-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of Third International Symposium on Time-Frequency and Time-Scale Analysis (TFTS-96)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/TFSA.1996.547208\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of Third International Symposium on Time-Frequency and Time-Scale Analysis (TFTS-96)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TFSA.1996.547208","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In this paper, we present a discrete rotational Gabor transform and apply it to signal representation on a non-rectangular time-frequency plane tiling. We use a modified version of the recently proposed discrete rotational Fourier transform. The rotational Gabor transform decomposes a signal using as basis functions scaled, translated and modulated by linear chirps windows. As a result, the time-varying frequency content of a signal is represented better than with sinusoidal modulated expansions. For a multi-component signal, the Gabor coefficients are obtained by combining different tilings to maximize a local energy concentration measure. This permits us to achieve a highly localized time-frequency signal representation. Examples are given to illustrate the transformation.