Abstract
The occupancy and activity estimation are fields that have been severally researched in the past few years. However, the different techniques used include a mixture of atmospheric features such as humidity and temperature, many devices such as cameras and audio sensors, or they are limited to speech recognition. In this work is proposed that the occupancy and activity can be estimated only from the audio information using an automatic approach of audio feature engineering to extract, analyze and select descriptors/variables. This scheme of extraction of audio descriptors is used to determine the occupation and activity in specific smart environments, such that our approach can differentiate between academic, administrative or commercial environments. Our approach from the audio feature engineering is compared to previous similar works on occupancy estimation and/or activity estimation in smart buildings (most of them including other features, such as atmospherics and visuals). In general, the results obtained are very encouraging compared to previous studies.
Highlights
We propose an audio descriptor engineering methodology for occupancy estimation using time series descriptors and compare different prediction techniques
While other works use information from different sources, our approach only uses audio descriptors, which is useful in most cases where the only information available is auditory
That is the main contribution of our work, we propose an estimation approach with acceptable accuracy from audio information
Summary
Based on the previous work of Jimenez et al [1], this article proposes the research in occupancy and activity estimation for smart buildings using audio information
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