{"title":"基于Mel频率倒谱系数(MFCC)特征提取和帧特征选择的早安晚安问候语分类","authors":"H. Heriyanto","doi":"10.31315/TELEMATIKA.V18I1.4495","DOIUrl":null,"url":null,"abstract":"Purpose:Select the right features on the frame for good accuracyDesign/methodology/approach:Extraction of Mel Frequency Cepstral Coefficient (MFCC) Features and Selection of Dominant Weight Normalized (DWN) FeaturesFindings/result:The accuracy results show that the MFCC method with the 9th frame selection has a higher accuracy rate of 85% compared to other frames.Originality/value/state of the art:Selection of the appropriate features on the frame.","PeriodicalId":31716,"journal":{"name":"Telematika","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2021-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Good Morning to Good Night Greeting Classification Using Mel Frequency Cepstral Coefficient (MFCC) Feature Extraction and Frame Feature Selection\",\"authors\":\"H. Heriyanto\",\"doi\":\"10.31315/TELEMATIKA.V18I1.4495\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Purpose:Select the right features on the frame for good accuracyDesign/methodology/approach:Extraction of Mel Frequency Cepstral Coefficient (MFCC) Features and Selection of Dominant Weight Normalized (DWN) FeaturesFindings/result:The accuracy results show that the MFCC method with the 9th frame selection has a higher accuracy rate of 85% compared to other frames.Originality/value/state of the art:Selection of the appropriate features on the frame.\",\"PeriodicalId\":31716,\"journal\":{\"name\":\"Telematika\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-03-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Telematika\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.31315/TELEMATIKA.V18I1.4495\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Telematika","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.31315/TELEMATIKA.V18I1.4495","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
摘要
设计/方法/方法:Mel Frequency Cepstral Coefficient (MFCC) feature的提取和Dominant Weight Normalized (DWN) feature的选择结果/结果:准确率结果表明，第9帧选择的MFCC方法与其他帧相比准确率更高，达到85%。原创性/价值/艺术水平:在框架上选择适当的特征。
Good Morning to Good Night Greeting Classification Using Mel Frequency Cepstral Coefficient (MFCC) Feature Extraction and Frame Feature Selection
Purpose:Select the right features on the frame for good accuracyDesign/methodology/approach:Extraction of Mel Frequency Cepstral Coefficient (MFCC) Features and Selection of Dominant Weight Normalized (DWN) FeaturesFindings/result:The accuracy results show that the MFCC method with the 9th frame selection has a higher accuracy rate of 85% compared to other frames.Originality/value/state of the art:Selection of the appropriate features on the frame.
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