Audio based surveillance forcognitive assistance using a CMT microphone within socially assistive technology.
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 15 degrees . 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.
Rougui
J E
JE
LRIT-ESIGETEL, 1, Rue du Port de Valvins, 77210 Fontainebleau-Avon Cedex, France. jamal.rougui@esigetel.fr
Istrate
D
D
Souidene
W
W
Opitz
M
M
Riemann
M
M
D000162:Acoustics; D000465:Algorithms; D003072:Cognition Disorders / Q000534:rehabilitation*; D003198:Computer Simulation; D001330:Electronic Data Processing; D004867:Equipment Design; D006801:Humans; D016011:Normal Distribution; D012656:Self-Help Devices; D012815:Signal Processing, Computer-Assisted; D012984:Software; D013016:Sound; D013017:Sound Localization; D013997:Time Factors