Title :
AmbientSense: A real-time ambient sound recognition system for smartphones
Author :
Rossi, Mattia ; Feese, Sebastian ; Amft, Oliver ; Braune, Nils ; Martis, Sandro ; Troster, G.
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.
Keywords :
audio signal processing; feature extraction; microphones; signal classification; smart phones; AmbientSense; Android app; Google Nexus One phone; Samsung Galaxy SII smartphone; ambient sound analysis; ambient sound class; audio feature transmission; autonomous mode processing; classification results; phone CPU load; real time feedback; real-time ambient sound recognition system; recognition delay; recognition performance; runtime load; server mode recognition; smartphone microphone; user context recognition; Feature extraction; Real-time systems; Runtime; Servers; Smart phones; Support vector machines; Training;
Conference_Titel :
Pervasive Computing and Communications Workshops (PERCOM Workshops), 2013 IEEE International Conference on
Conference_Location :
San Diego, CA
Print_ISBN :
978-1-4673-5075-4
Electronic_ISBN :
978-1-4673-5076-1
DOI :
10.1109/PerComW.2013.6529487
