Auditory Scene Analysis-Based Feature Extraction for Indoor Subarea Localization Using Smartphones

This paper considers the task of indoor subarea localization by acoustic sensing. In many practical scenarios, such as ordinary office rooms, subarea localization based on a single acoustic property without subsidiaries is always tricky due to similar space physical structures. To combat the indoor subarea localization problem without any dedicated devices, we propose a framework allowing the ordinary smartphone to quickly and easily determine its indoor subarea location. The proposed structure is to first construct the multidimensional fingerprint of each environmental background audio in a room using the auditory scene analysis method; then extract the room-level fingerprint by the acoustic background spectrum method, and cluster the subarea-level fingerprints using the Pearson correlation coefficient technology; subsequently, together with the offline stage long short-term memory recurrent neuron network and online stage least variance algorithm, the effective indoor subarea localization was achieved; finally, the localization results are presented not only in text form but also in a privacy-protected color sound pattern which is built by chroma DCT-reduced log pitch. Our experiments with real-world audios, which include 96-h environment background audios of 14 different rooms, presents that the room-level localization accuracy was higher than 90% and the subarea-level accuracy could be up to 97.64%. These results strongly proved that the proposed framework could perform well even in small complicated rooms.