Robust and Responsive Acoustic Pairing of Devices Using Decorrelating Time-Frequency Modelling. Perez, P.; Backstrom, T.; and Sigg, S. In 27th European Signal Processing Conference (EUSIPCO), 2019.
Robust and Responsive Acoustic Pairing of Devices Using Decorrelating Time-Frequency Modelling [pdf]Paper  abstract   bibtex   1 download  
Voice user interfaces have increased in popularity, as they enable natural interaction with different applications using one’s voice. To improve their usability and audio quality, several devices could interact to provide a unified voice user interface. However, with devices cooperating and sharing voice-related information, user privacy may be at risk. Therefore, access management rules that preserve user privacy are important. State-of-the-art methods for acoustic pairing of devices provide fingerprinting based on the time-frequency representation of the acoustic signal and error-correction. We propose to use such acoustic fingerprinting to authorise devices which are acoustically close. We aim to obtain fingerprints of ambient audio adapted to the requirements of voice user interfaces. Our experiments show that the responsiveness and robustness is improved by combining overlapping windows and decorrelating transforms.
@InProceedings{Perez_2019_eusipco,
author={Pablo Perez and Tom Backstrom and Stephan Sigg},
title={Robust and Responsive Acoustic Pairing of Devices Using Decorrelating Time-Frequency Modelling},
booktitle={27th European Signal Processing Conference (EUSIPCO)},
year={2019},
abstract = {Voice user interfaces have increased in popularity, as
they enable natural interaction with different applications using
one’s voice. To improve their usability and audio quality, several
devices could interact to provide a unified voice user interface.
However, with devices cooperating and sharing voice-related
information, user privacy may be at risk. Therefore, access
management rules that preserve user privacy are important.
State-of-the-art methods for acoustic pairing of devices provide
fingerprinting based on the time-frequency representation of the
acoustic signal and error-correction. We propose to use such
acoustic fingerprinting to authorise devices which are acoustically
close. We aim to obtain fingerprints of ambient audio adapted to
the requirements of voice user interfaces. Our experiments show
that the responsiveness and robustness is improved by combining
overlapping windows and decorrelating transforms.},
url_Paper = {http://ambientintelligence.aalto.fi/paper/perezEusipco2019.pdf},
group = {ambience}}
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