Abstract
Context recognition in indoor and outdoor surroundings is an important area of research for the development of autonomous systems. This work describes an approach to the classification of audio signals found in both indoor and outdoor environments. Several audio features are extracted from raw signals. We analyze the relevance and importance of these features and use that information to design a multi-stage classifier architecture. Our results show that the multi-stage classification scheme is superior than a single stage classifier and it generates an 80% success rate on a 7 class problem.
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Lopes, J., Lin, C., Singh, S. (2006). Multi-stage Classification for Audio Based Activity Recognition. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2006. IDEAL 2006. Lecture Notes in Computer Science, vol 4224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875581_100
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DOI: https://doi.org/10.1007/11875581_100
Publisher Name: Springer, Berlin, Heidelberg
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