Recognizing Daily Life Context using Web-Collected Audio Data

Publication Type Conference Paper
Authors Mirco Rossi, Gerhard Tröster, Oliver Amft
Title Recognizing Daily Life Context using Web-Collected Audio Data
Abstract 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%.
Date 2012
Proceedings Title Proceedings of the IEEE International Symposium on Wearable Computers (ISWC '12)
Publisher IEEE
Pages 25–28
DOI 10.1109/ISWC.2012.12
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Friedrich-Alexander-Universität Erlangen-Nürnberg