{"title":"利用经子带能量转换改进的特征识别咳嗽","authors":"Chunmei Zhu, Lianfang Tian, Xiangyang Li, Hongqiang Mo, Zeguang Zheng","doi":"10.1109/BMEI.2013.6746943","DOIUrl":null,"url":null,"abstract":"The purpose of this paper is to improve mel frequency cepstrum coefficients (MFCCs) for cough recognition. To highlight high energy, the most remarkable characteristic of cough sound, we propose a method of sub-band energy transformation to improve traditional MFCCs. This method enhances bands with high energy and ignores the ones with low energy according to the sub-band energy distribution acquired by investigation of varieties of cough sounds. Cough recognition experiments using hidden Markov models (HMMs) show that the average recognition rate rises from 87% to 91% and robustness of the system in noisy environment is improved by the proposed method.","PeriodicalId":163211,"journal":{"name":"2013 6th International Conference on Biomedical Engineering and Informatics","volume":"85 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"Recognition of cough using features improved by sub-band energy transformation\",\"authors\":\"Chunmei Zhu, Lianfang Tian, Xiangyang Li, Hongqiang Mo, Zeguang Zheng\",\"doi\":\"10.1109/BMEI.2013.6746943\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The purpose of this paper is to improve mel frequency cepstrum coefficients (MFCCs) for cough recognition. To highlight high energy, the most remarkable characteristic of cough sound, we propose a method of sub-band energy transformation to improve traditional MFCCs. This method enhances bands with high energy and ignores the ones with low energy according to the sub-band energy distribution acquired by investigation of varieties of cough sounds. Cough recognition experiments using hidden Markov models (HMMs) show that the average recognition rate rises from 87% to 91% and robustness of the system in noisy environment is improved by the proposed method.\",\"PeriodicalId\":163211,\"journal\":{\"name\":\"2013 6th International Conference on Biomedical Engineering and Informatics\",\"volume\":\"85 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 6th International Conference on Biomedical Engineering and Informatics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/BMEI.2013.6746943\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 6th International Conference on Biomedical Engineering and Informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BMEI.2013.6746943","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Recognition of cough using features improved by sub-band energy transformation
The purpose of this paper is to improve mel frequency cepstrum coefficients (MFCCs) for cough recognition. To highlight high energy, the most remarkable characteristic of cough sound, we propose a method of sub-band energy transformation to improve traditional MFCCs. This method enhances bands with high energy and ignores the ones with low energy according to the sub-band energy distribution acquired by investigation of varieties of cough sounds. Cough recognition experiments using hidden Markov models (HMMs) show that the average recognition rate rises from 87% to 91% and robustness of the system in noisy environment is improved by the proposed method.