  This paper outlines preliminary steps towards the development of an audio-
based room-occupancy analysis model. Our approach borrows from speech
recognition tradition and is based on Gaussian Mixtures and Hidden Markov
Models. We analyze possible challenges encountered in the development of such a
model, and offer several solutions including feature design and prediction
strategies. We provide results obtained from experiments with audio data from a
retail store in Palo Alto, California. Model assessment is done via
leave-two-out Bootstrap and model convergence achieves good accuracy, thus
representing a contribution to multimodal people counting algorithms.
