M. Rossi, S. Feese, O. Amft, Nils Braune, S. Martis, G. Tröster
{"title":"AmbientSense: A real-time ambient sound recognition system for smartphones","authors":"M. Rossi, S. Feese, O. Amft, Nils Braune, S. Martis, G. Tröster","doi":"10.1109/PerComW.2013.6529487","DOIUrl":null,"url":null,"abstract":"This paper presents design, implementation, and evaluation of AmbientSense, a real-time ambient sound recognition system on a smartphone. AmbientSense continuously recognizes user context by analyzing ambient sounds sampled from a smartphone's microphone. The phone provides a user with realtime feedback on recognised context. AmbientSense is implemented as an Android app and works in two modes: in autonomous mode processing is performed on the smartphone only. In server mode recognition is done by transmitting audio features to a server and receiving classification results back. We evaluated both modes in a set of 23 daily life ambient sound classes and describe recognition performance, phone CPU load, and recognition delay. The application runs with a fully charged battery up to 13.75 h on a Samsung Galaxy SII smartphone and up to 12.87 h on a Google Nexus One phone. Runtime and CPU load were similar for autonomous and server modes.","PeriodicalId":101502,"journal":{"name":"2013 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"71","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PerComW.2013.6529487","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 71
Abstract
This paper presents design, implementation, and evaluation of AmbientSense, a real-time ambient sound recognition system on a smartphone. AmbientSense continuously recognizes user context by analyzing ambient sounds sampled from a smartphone's microphone. The phone provides a user with realtime feedback on recognised context. AmbientSense is implemented as an Android app and works in two modes: in autonomous mode processing is performed on the smartphone only. In server mode recognition is done by transmitting audio features to a server and receiving classification results back. We evaluated both modes in a set of 23 daily life ambient sound classes and describe recognition performance, phone CPU load, and recognition delay. The application runs with a fully charged battery up to 13.75 h on a Samsung Galaxy SII smartphone and up to 12.87 h on a Google Nexus One phone. Runtime and CPU load were similar for autonomous and server modes.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
