Using RFID tags as reference for phone location and orientation in daily life
Proceedings of the 4th Augmented Human International Conference
Combining crowd-generated media and personal data: semi-supervised learning for context recognition
Proceedings of the 1st ACM international workshop on Personal data meets distributed multimedia
Towards scalable activity recognition: adapting zero-effort crowdsourced acoustic models
Proceedings of the 12th International Conference on Mobile and Ubiquitous Multimedia
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This work presents an approach to model daily life contexts from web-collected audio data. Being available in vast quantities from many different sources, audio data from the web provides heterogeneous training data to construct recognition systems. Crowd-sourced textual descriptions (tags) related to individual sound samples were used in a configurable recognition system to model 23 sound context categories. We analysed our approach using different outlier filtering techniques with dedicated recordings of all 23 categories and in a study with 230 hours of full-day recordings of 10 participants using smart phones. Depending on the outlier technique, our system achieved recognition accuracies between 51% and 80%.