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Automatic context recognition enables mobile devices to react to changes in the environment and different situations. While many different sensors can be used for context recognition, the use of acoustic cues is among the most popular and successful. Current approaches to acoustic context recognition (ACR) are too costly in terms of computation and memory requirements to support an always-listening mode. This paper describes our work to develop a reduced complexity, efficient approach to ACR involving support vector machine classifiers. The principal hypothesis is that a significant fraction of training data contains information redundant to classification. Through clustering, training data can thus be selectively decimated in order to reduce the number of support vectors needed to represent discriminative hyperplanes. This represents a significant saving in terms of computational and memory efficiency, with only modest degradations in classification accuracy.

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