{"title":"介绍离散Morphlet变换及其在语音转换中的应用","authors":"L. S. Vieira, R. Guido, Shi-Huang Chen","doi":"10.1109/ISM.2011.93","DOIUrl":null,"url":null,"abstract":"This paper introduces Morph let, a new wavelet transform adapted for voice conversion purposes. The paradigm of joint time-frequency-shape analysis of discrete-time signals, possible by means of the Discrete Shape let Transform (DST), is the basis used for the construction of Morph lets. The results assure the efficacy of the proposed transform, which is able, by itself and with the help of no other tool such as a neural network, to carry out the task, totally.","PeriodicalId":339410,"journal":{"name":"2011 IEEE International Symposium on Multimedia","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Introducing the Discrete Morphlet Transform and its Applications for Voice Conversion\",\"authors\":\"L. S. Vieira, R. Guido, Shi-Huang Chen\",\"doi\":\"10.1109/ISM.2011.93\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper introduces Morph let, a new wavelet transform adapted for voice conversion purposes. The paradigm of joint time-frequency-shape analysis of discrete-time signals, possible by means of the Discrete Shape let Transform (DST), is the basis used for the construction of Morph lets. The results assure the efficacy of the proposed transform, which is able, by itself and with the help of no other tool such as a neural network, to carry out the task, totally.\",\"PeriodicalId\":339410,\"journal\":{\"name\":\"2011 IEEE International Symposium on Multimedia\",\"volume\":\"7 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-12-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 IEEE International Symposium on Multimedia\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISM.2011.93\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 IEEE International Symposium on Multimedia","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISM.2011.93","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Introducing the Discrete Morphlet Transform and its Applications for Voice Conversion
This paper introduces Morph let, a new wavelet transform adapted for voice conversion purposes. The paradigm of joint time-frequency-shape analysis of discrete-time signals, possible by means of the Discrete Shape let Transform (DST), is the basis used for the construction of Morph lets. The results assure the efficacy of the proposed transform, which is able, by itself and with the help of no other tool such as a neural network, to carry out the task, totally.
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