An Investigation of Crowd Speech for Room Occupancy Estimation

Room occupancy estimation technology has been shown to reduce building energy cost significantly. However speechbased occupancy estimation has not been well explored. In this paper, we investigate energy mode and babble speaker count methods for estimating both small and large crowds in a party-mode room setting. We also examine how distance between speakers and microphone affects their estimation accuracies. Then we propose a novel entropy-based method, which is invariant to different speakers and their different positions in a room. Evaluations on synthetic crowd speech generated using the TIMIT corpus show that acoustic volume features are less affected by distance, and our proposed method outperforms existing methods across a range of different conditions.