Sensor data acquired from multiple sensors simultaneously is featuring increasingly in our evermore pervasive world. Buildings can be made smarter and more e�cient, spaces more responsive to users. A fundamental building block towards smart spaces is the ability to understand who is present in a certain area. A ubiquitous way of detecting this is to exploit the unique vocal features as people interact with one another. As an example, consider audio features sampled during a meeting, yielding a noisy set of possible voiceprints. With a number of meetings and knowledge of participation (e.g. through a calendar or MAC address), can we learn to associate a speci€c identity with a particular voiceprint? Obviously enrolling users into a biometric database is time-consuming and not robust to vocal deviations over time. To address this problem, the standard approach is to perform a clustering step (e.g. of audio data) followed by a data association step, when identity-rich sensor data is available. In this paper we show that this approach is not robust to noise in either type of sensor stream; to tackle this issue we propose a novel algorithm that jointly optimises the clustering and association process yielding up to three times higher identi€cation precision than approaches that execute these steps sequentially. We demonstrate the performance bene€ts of our approach in two case studies, one with acoustic and MAC datasets that we collected from meetings in a non-residential building, and another from an online dataset from recorded radio interviews.
|Title of host publication||Proceedings of the 16th ACM/IEEE International Conference on Information Processing in Sensor Networks|
|Publisher||Association for Computing Machinery|
|Number of pages||12|
|Publication status||Published - 18 Apr 2017|
Lu, X., Wen, H., Wang, S., Markham, A., & Trigoni, N. (2017). SCAN: Learning Associations between Noisy Sensor Sets. In Proceedings of the 16th ACM/IEEE International Conference on Information Processing in Sensor Networks (pp. 67-78). Association for Computing Machinery. https://doi.org/10.1145/3055031.3055073