  In the past few years, several case studies have illustrated that the use of
occupancy information in buildings leads to energy-efficient and low-cost HVAC
operation. The widely presented techniques for occupancy estimation include
temperature, humidity, CO2 concentration, image camera, motion sensor and
passive infrared (PIR) sensor. So far little studies have been reported in
literature to utilize audio and speech processing as indoor occupancy
prediction technique. With rapid advances of audio and speech processing
technologies, nowadays it is more feasible and attractive to integrate
audio-based signal processing component into smart buildings. In this work, we
propose to utilize audio processing techniques (i.e., speaker recognition and
background audio energy estimation) to estimate room occupancy (i.e., the
number of people inside a room). Theoretical analysis and simulation results
demonstrate the accuracy and effectiveness of this proposed occupancy
estimation technique. Based on the occupancy estimation, smart buildings will
adjust the thermostat setups and HVAC operations, thus, achieving greater
quality of service and drastic cost savings.
