Sound detection and classification through transient models usingwavelet coefficient trees

Medical Telesurvey needs human operator assistance by smart information systems. Usual sound classification may be applied to medical monitoring by use of microphones in patient's habitation. Detection is the first step of our sound analysis system and is necessary to extract the significant sounds before initiating the classification step. This paper proposes a detection method using transient models, based upon dyadic trees of wavelet coefficients to insure short detection delay. The classification stage uses a Gaussian Mixture Model classifier with classical acoustical parameters like MFCC. Detection and classification stages are evaluated in experimental recorded noise condition which is nonstationary and more aggressive than simulated white noise and fits with our application. Wavelet filtering methods are proposed to enhance performances in low signal to noise ratios.