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2021-01-20
Zarazaga, P. P., Bäckström, T., Sigg, S..  2020.  Acoustic Fingerprints for Access Management in Ad-Hoc Sensor Networks. IEEE Access. 8:166083—166094.

Voice user interfaces can offer intuitive interaction with our devices, but the usability and audio quality could be further improved if multiple devices could collaborate to provide a distributed voice user interface. To ensure that users' voices are not shared with unauthorized devices, it is however necessary to design an access management system that adapts to the users' needs. Prior work has demonstrated that a combination of audio fingerprinting and fuzzy cryptography yields a robust pairing of devices without sharing the information that they record. However, the robustness of these systems is partially based on the extensive duration of the recordings that are required to obtain the fingerprint. This paper analyzes methods for robust generation of acoustic fingerprints in short periods of time to enable the responsive pairing of devices according to changes in the acoustic scenery and can be integrated into other typical speech processing tools.

2020-08-03
Zarazaga, Pablo Pérez, B¨ackström, Tom, Sigg, Stephan.  2019.  Robust and Responsive Acoustic Pairing of Devices Using Decorrelating Time-Frequency Modelling. 2019 27th European Signal Processing Conference (EUSIPCO). :1–5.
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.