J. Montalvao, D. Istrate, J. Boudy and J. Mouba, "Sound Event Detection in Remote Health Care - Small Learning Datasets and Over Constrained Gaussian Mixture Models", EMBC 2010, August 31 - September 4, Buenos Aires, Argentine,pp.1146 - 1149
The use of Gaussian Mixture Models (GMM), adapted through the Expectation Minimization (EM) algorithm, is not rare in Audio Analysis for Surveillance Applications and Environmental sound recognition. Their use is founded on the good qualities of GMM models when aimed at approximating Probability Density Functions (PDF) of random variables. But in some cases, where models are to be adapted from small sample sets instead of large but generic databases, a problem of balance between model complexity and sample size may play an important role. From this perspective, we show, through simple sound classication experiments, that constrained GMM, with fewer degrees of freedom, as compared to GMM with full covariance matrices, provide better classication performances. Moreover, pushing this argument even further, we also show that a Parzen model can do even better than usual GMM.