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The authors study auditory context recognition for context-aware mobile computing systems. Auditory contexts are recordings of a mixture of sounds, or ambient audio, from mobile users' everyday environments. For training a classifier, a set of recordings from different environments are segmented and labeled. The segments are windowed into overlapping frames for feature extraction. While previous work in auditory context recognition has often treated the problem as a sequence classification task and used HMM-based classifiers to recognize a sequence of consecutive MFCCs of frames, they compute averaged Mel-spectrum over the segments and train a SVM-based classifier.
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