{"title":"G.728解码器的同步错误检测","authors":"A. Wachtler, R. Hoffmann","doi":"10.1109/ITS.1998.718436","DOIUrl":null,"url":null,"abstract":"A new method for the fast detection of frame synchronisation errors inside the G.728 speech decoder is described. The proposed algorithm uses a priori knowledge about internal decoder parameters. The G.728 frame synchronisation fails, if an octet slip in the transmission system occurs. This error type causes a shift of the word boundaries in the G.728 data stream. The decoder produces in this case a loud and noisy output signal. The proposed algorithm can be implemented as an add-on in a regular G.728 decoder, without changing the standard operation of the codec. Three classification methods, the Euclidian distance classifier, McCulloch-Pitts network and feedforward neural network are compared with respect to their performance and complexity.","PeriodicalId":205350,"journal":{"name":"ITS'98 Proceedings. SBT/IEEE International Telecommunications Symposium (Cat. No.98EX202)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1998-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Detection of synchronisation errors for the G.728 decoder\",\"authors\":\"A. Wachtler, R. Hoffmann\",\"doi\":\"10.1109/ITS.1998.718436\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A new method for the fast detection of frame synchronisation errors inside the G.728 speech decoder is described. The proposed algorithm uses a priori knowledge about internal decoder parameters. The G.728 frame synchronisation fails, if an octet slip in the transmission system occurs. This error type causes a shift of the word boundaries in the G.728 data stream. The decoder produces in this case a loud and noisy output signal. The proposed algorithm can be implemented as an add-on in a regular G.728 decoder, without changing the standard operation of the codec. Three classification methods, the Euclidian distance classifier, McCulloch-Pitts network and feedforward neural network are compared with respect to their performance and complexity.\",\"PeriodicalId\":205350,\"journal\":{\"name\":\"ITS'98 Proceedings. SBT/IEEE International Telecommunications Symposium (Cat. No.98EX202)\",\"volume\":\"4 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1998-08-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ITS'98 Proceedings. SBT/IEEE International Telecommunications Symposium (Cat. No.98EX202)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ITS.1998.718436\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ITS'98 Proceedings. SBT/IEEE International Telecommunications Symposium (Cat. No.98EX202)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ITS.1998.718436","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Detection of synchronisation errors for the G.728 decoder
A new method for the fast detection of frame synchronisation errors inside the G.728 speech decoder is described. The proposed algorithm uses a priori knowledge about internal decoder parameters. The G.728 frame synchronisation fails, if an octet slip in the transmission system occurs. This error type causes a shift of the word boundaries in the G.728 data stream. The decoder produces in this case a loud and noisy output signal. The proposed algorithm can be implemented as an add-on in a regular G.728 decoder, without changing the standard operation of the codec. Three classification methods, the Euclidian distance classifier, McCulloch-Pitts network and feedforward neural network are compared with respect to their performance and complexity.