{"title":"比较时频分析方法在语音编码中的应用","authors":"S. Bousselmi, K. Ouni","doi":"10.1109/SETIT.2016.7939908","DOIUrl":null,"url":null,"abstract":"The goal of this paper is to compare the performance of two time-frequency decomposition in a context of speech coding. These decompositions are based on wavelet and wavelet frame theory. The main advantages of wavelet frame compared to wavelet are perfect reconstruction, resilience to quantization noise, nearly shift-invariant, symmetry and good time-frequency localization. The evaluation tests reveal that the quality of coded speech using the tight framelets packet transform outperform that of the wavelets packet transform.","PeriodicalId":426951,"journal":{"name":"2016 7th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT)","volume":"100 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"The comparison of time-frequency analysis methods for speech coding application\",\"authors\":\"S. Bousselmi, K. Ouni\",\"doi\":\"10.1109/SETIT.2016.7939908\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The goal of this paper is to compare the performance of two time-frequency decomposition in a context of speech coding. These decompositions are based on wavelet and wavelet frame theory. The main advantages of wavelet frame compared to wavelet are perfect reconstruction, resilience to quantization noise, nearly shift-invariant, symmetry and good time-frequency localization. The evaluation tests reveal that the quality of coded speech using the tight framelets packet transform outperform that of the wavelets packet transform.\",\"PeriodicalId\":426951,\"journal\":{\"name\":\"2016 7th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT)\",\"volume\":\"100 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 7th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SETIT.2016.7939908\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 7th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SETIT.2016.7939908","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The comparison of time-frequency analysis methods for speech coding application
The goal of this paper is to compare the performance of two time-frequency decomposition in a context of speech coding. These decompositions are based on wavelet and wavelet frame theory. The main advantages of wavelet frame compared to wavelet are perfect reconstruction, resilience to quantization noise, nearly shift-invariant, symmetry and good time-frequency localization. The evaluation tests reveal that the quality of coded speech using the tight framelets packet transform outperform that of the wavelets packet transform.
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