Auditory Scene Analysis-Based Feature Extraction for Indoor Subarea Localization Using Smartphones
Xiyu Song, Mei Wang, Hongbing Qiu, Kaihua Li, Chen Ang · IEEE Sensors Journal · 2019
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.