CORAL: Recognition and Locating of Contextual Objects With Unmodulated Acoustic Signals | IEEE Journals & Magazine | IEEE Xplore

CORAL: Recognition and Locating of Contextual Objects With Unmodulated Acoustic Signals


Abstract:

The location context can benefit a broad range of context-aware applications, where recognizing and locating contextual objects, such as hair dryers, coffee machines, or ...Show More

Abstract:

The location context can benefit a broad range of context-aware applications, where recognizing and locating contextual objects, such as hair dryers, coffee machines, or water faucets, which are not equipped with any smart modules and thus unable to emit modulated signals, provide fine-grained contextual information. While there have been extensive researches on localizing smart mobile devices, little has been done for locating contextual objects, let alone for recognizing and locating them together. In this article, we aim to study the problem of simultaneously recognizing and locating such contextual objects and present CORAL, a contextual object recognition and locating scheme by the usage of unmodulated acoustic signals from the working contextual objects recorded by the commercial off-the-shelf smartphones of users. Specifically, CORAL exploits the frequency and power features of these signals to build a mel-frequency cepstral coefficients (MFCCs) data set for contextual objects, and constructs a classifier for contextual object recognition by using bidirectional LSTM (BiLSTM) and a regression model for object-to-device distance computation by using LightGBM, which is then used for object locating with the help of the user’s trace. We implement a prototype of CORAL and extensive experiments show that the CORAL achieves high recognition accuracy and locating accuracy, even when there are concurrent working contextual objects or ambient noises.
Published in: IEEE Internet of Things Journal ( Volume: 11, Issue: 20, 15 October 2024)
Page(s): 33734 - 33743
Date of Publication: 18 July 2024

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