Title :
Audio based surveillance forcognitive assistance using a CMT microphone within socially assistive technology
Author :
Rougui, J.E. ; Istrate, D. ; Souidene, W. ; Opitz, M. ; Riemann, M.
Author_Institution :
LRIT-ESIGETEL, Fontainebleau-Avon, France
Abstract :
This work proposes a system for Acoustic Event Detection and Classification (AEDC) using enhanced audio signal provided by a CMT (Coincidence Microphone Technology) microphone. The CMT microphone through signal processing algorithm provides an enhanced signal in several azimuths with a step of 15deg. The AEC module exploits this technology to increase classification performance. The automatic detection system based on DWT uses an adaptive threshold for a different energy level and sampling rate quality. The classification system is based on an unsupervised order estimation of Gaussian mixture model adapted to the variability of sound event acoustic information and the representation cost.
Keywords :
Gaussian distribution; biomedical ultrasonics; medical signal processing; microphones; neurophysiology; CMT microphone; Gaussian mixture model; acoustic event classification; acoustic event detection; audio based surveillance; cognitive assistance; coincidence microphone technology; energy level; socially assistive technology; sound event acoustic information; Acoustics; Algorithms; Automatic Data Processing; Cognition Disorders; Computer Simulation; Equipment Design; Humans; Normal Distribution; Self-Help Devices; Signal Processing, Computer-Assisted; Software; Sound; Sound Localization; Time Factors;
Conference_Titel :
Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE
Conference_Location :
Minneapolis, MN
Print_ISBN :
978-1-4244-3296-7
Electronic_ISBN :
1557-170X
DOI :
10.1109/IEMBS.2009.5334762