{"title":"基于神经网络的麦克风阵列输入信号时空模式学习","authors":"Akihiro Iseki, K. Ozawa, Yuichiro Kinoshita","doi":"10.1109/GCCE.2014.7031270","DOIUrl":null,"url":null,"abstract":"A sharp directional microphone array system was previously developed using a neural network. However, the system cannot distinguish two signals with different frequencies because it learns only the spatial pattern of the sound pressure distribution of the input signals. To overcome this problem, herein we propose a system that learns the temporal-spatial pattern of the input signals. The proposed system successfully obtains a wide-band super-directivity.","PeriodicalId":145771,"journal":{"name":"2014 IEEE 3rd Global Conference on Consumer Electronics (GCCE)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Neural network-based microphone array learning of temporal-spatial patterns of input signals\",\"authors\":\"Akihiro Iseki, K. Ozawa, Yuichiro Kinoshita\",\"doi\":\"10.1109/GCCE.2014.7031270\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A sharp directional microphone array system was previously developed using a neural network. However, the system cannot distinguish two signals with different frequencies because it learns only the spatial pattern of the sound pressure distribution of the input signals. To overcome this problem, herein we propose a system that learns the temporal-spatial pattern of the input signals. The proposed system successfully obtains a wide-band super-directivity.\",\"PeriodicalId\":145771,\"journal\":{\"name\":\"2014 IEEE 3rd Global Conference on Consumer Electronics (GCCE)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 IEEE 3rd Global Conference on Consumer Electronics (GCCE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/GCCE.2014.7031270\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE 3rd Global Conference on Consumer Electronics (GCCE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/GCCE.2014.7031270","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Neural network-based microphone array learning of temporal-spatial patterns of input signals
A sharp directional microphone array system was previously developed using a neural network. However, the system cannot distinguish two signals with different frequencies because it learns only the spatial pattern of the sound pressure distribution of the input signals. To overcome this problem, herein we propose a system that learns the temporal-spatial pattern of the input signals. The proposed system successfully obtains a wide-band super-directivity.