# ABROA : Audio-Based Room-Occupancy Analysis using Gaussian Mixtures and Hidden Markov Models

arXiv: https://arxiv.org/abs/1607.07801 · HTML: https://mlsanity.com/paper/1607.07801/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jun 2016.

## Built on by (most cited first, 1 of 1)

- [Crowdotic: A Privacy-Preserving Hospital Waiting Room Crowd Density Estimation with Non-speech Audio](https://mlsanity.com/paper/2309.10280/) (2023)

## Abstract (arXiv)

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.