K. Teeravajanadet, N. Siwilai, K. Thanaselanggul, N. Ponsiricharoenphan, S. Tungjitkusolmun, P. Phasukkit
{"title":"基于卷积神经网络的婴儿哭声识别方法","authors":"K. Teeravajanadet, N. Siwilai, K. Thanaselanggul, N. Ponsiricharoenphan, S. Tungjitkusolmun, P. Phasukkit","doi":"10.1109/BMEiCON47515.2019.8990191","DOIUrl":null,"url":null,"abstract":"In this paper, an investigation of crying signal spectra is used to classify categories of infant cries. Three different types of crying considered in this work are hungry, sleepy and burping need. These cries are preprocessed and converted for calculation of Mel-Frequency Cepstral Coefficients (MFCC) before being classified by Convolutional Neural Network (CNN). Experimental results show that CNN based deep learning achieves high performance of 84%.","PeriodicalId":213939,"journal":{"name":"2019 12th Biomedical Engineering International Conference (BMEiCON)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"An Infant Cry Recognition based on Convolutional Neural Network Method\",\"authors\":\"K. Teeravajanadet, N. Siwilai, K. Thanaselanggul, N. Ponsiricharoenphan, S. Tungjitkusolmun, P. Phasukkit\",\"doi\":\"10.1109/BMEiCON47515.2019.8990191\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, an investigation of crying signal spectra is used to classify categories of infant cries. Three different types of crying considered in this work are hungry, sleepy and burping need. These cries are preprocessed and converted for calculation of Mel-Frequency Cepstral Coefficients (MFCC) before being classified by Convolutional Neural Network (CNN). Experimental results show that CNN based deep learning achieves high performance of 84%.\",\"PeriodicalId\":213939,\"journal\":{\"name\":\"2019 12th Biomedical Engineering International Conference (BMEiCON)\",\"volume\":\"13 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 12th Biomedical Engineering International Conference (BMEiCON)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/BMEiCON47515.2019.8990191\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 12th Biomedical Engineering International Conference (BMEiCON)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BMEiCON47515.2019.8990191","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An Infant Cry Recognition based on Convolutional Neural Network Method
In this paper, an investigation of crying signal spectra is used to classify categories of infant cries. Three different types of crying considered in this work are hungry, sleepy and burping need. These cries are preprocessed and converted for calculation of Mel-Frequency Cepstral Coefficients (MFCC) before being classified by Convolutional Neural Network (CNN). Experimental results show that CNN based deep learning achieves high performance of 84%.
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