1,552 research outputs found
Deep Room Recognition Using Inaudible Echos
Recent years have seen the increasing need of location awareness by mobile
applications. This paper presents a room-level indoor localization approach
based on the measured room's echos in response to a two-millisecond single-tone
inaudible chirp emitted by a smartphone's loudspeaker. Different from other
acoustics-based room recognition systems that record full-spectrum audio for up
to ten seconds, our approach records audio in a narrow inaudible band for 0.1
seconds only to preserve the user's privacy. However, the short-time and
narrowband audio signal carries limited information about the room's
characteristics, presenting challenges to accurate room recognition. This paper
applies deep learning to effectively capture the subtle fingerprints in the
rooms' acoustic responses. Our extensive experiments show that a two-layer
convolutional neural network fed with the spectrogram of the inaudible echos
achieve the best performance, compared with alternative designs using other raw
data formats and deep models. Based on this result, we design a RoomRecognize
cloud service and its mobile client library that enable the mobile application
developers to readily implement the room recognition functionality without
resorting to any existing infrastructures and add-on hardware.
Extensive evaluation shows that RoomRecognize achieves 99.7%, 97.7%, 99%, and
89% accuracy in differentiating 22 and 50 residential/office rooms, 19 spots in
a quiet museum, and 15 spots in a crowded museum, respectively. Compared with
the state-of-the-art approaches based on support vector machine, RoomRecognize
significantly improves the Pareto frontier of recognition accuracy versus
robustness against interfering sounds (e.g., ambient music).Comment: 29 page
Acoustical Ranging Techniques in Embedded Wireless Sensor Networked Devices
Location sensing provides endless opportunities for a wide range of applications in GPS-obstructed environments;
where, typically, there is a need for higher degree of accuracy. In this article, we focus on robust range
estimation, an important prerequisite for fine-grained localization. Motivated by the promise of acoustic in
delivering high ranging accuracy, we present the design, implementation and evaluation of acoustic (both
ultrasound and audible) ranging systems.We distill the limitations of acoustic ranging; and present efficient
signal designs and detection algorithms to overcome the challenges of coverage, range, accuracy/resolution,
tolerance to Doppler’s effect, and audible intensity. We evaluate our proposed techniques experimentally on
TWEET, a low-power platform purpose-built for acoustic ranging applications. Our experiments demonstrate
an operational range of 20 m (outdoor) and an average accuracy 2 cm in the ultrasound domain. Finally,
we present the design of an audible-range acoustic tracking service that encompasses the benefits of a near-inaudible
acoustic broadband chirp and approximately two times increase in Doppler tolerance to achieve better performance
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