A dynamic equalizer system with person flow and semantic analysis for recognizing environmental situations

This paper proposes a new dynamic equalizer audio player system for recognizing an environmental situation naturally and easily using psychoacoustics, sensors and semantic analysis. Many people currently listen to audio while moving in a public space, such as a train station, airport, etc. However, sometimes they fail to recognize abnormal, dangerous situations because they are focused on listening to the audio. We focus on a real-time semantic analysis, diagnosing the environmental situation using Wi-Fi packet sensors and sending notifications with regard to the environment via audio changes using a dynamic equalizer function. The main features of this system are as follows: (i) measuring a person's movements while estimating his/her position, assessing a person's attributes such as the frequency of arrivals and determining whether the person is a foreign tourist, and analyzing the environmental situations in real time using Wi-Fi packet sensors; (ii) time-series processing the building's available databases with congestion diagnosis while detecting abnormal situations, and (iii) equalizing for recognition of the environmental situation using psychoacoustics. Our approach has been illustrated using real collected data, showing that using sensors increases the cost-effectiveness and data accuracy. The results indicate that the real-time processing of environmental data and personal movement information based on semantic technologies is possible. The system can be used at any location, such as in train stations, shopping malls, theme parks, social events, etc. The methodology not only promotes user safety and comfort but can also be used to manage public safety and make traffic congestion monitoring scalable and low-latency.