Audio-based human activity recognition using Non-Markovian Ensemble Voting


Human activity recognition is a key component for socially enabled robots to effectively and naturally interact with humans. In this paper we exploit the fact that many human activities produce characteristic sounds from which a robot can infer the corresponding actions. We propose a novel recognition approach called Non-Markovian Ensemble Voting (NEV) able to classify multiple human activities in an on­ line fashion without the need for silence detection or audio stream segmentation. Moreover, the method can deal with activities that are extended over undefined periods in time. In a series of experiments in real reverberant environments, we are able to robustly recognize 22 different sounds that correspond to a number of human activities in a bathroom and kitchen context. Our method outperforms several established classification techniques.

DOI: 10.1109/ROMAN.2012.6343802

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@inproceedings{Stork2012AudiobasedHA, title={Audio-based human activity recognition using Non-Markovian Ensemble Voting}, author={Johannes Andreas Stork and Luciano Spinello and Jens Silva and Kai Oliver Arras}, booktitle={RO-MAN}, year={2012} }