Machine listening is an area of study which is rapidly increasing in importance. The proliferation of massive sensory corpora, together with the perceptual needs of smart computational devices and smart spaces has lead to this increase. Machine listening provides both a computationally cheap alternative to machine vision, and a source of information that is complementary to visual information; hence, perceptual systems which lack the ability to process auditory information will in general perform less well than those which can process auditory information. Machine listening is also interesting in its own right, as research into computational auditory processing can help to shed light on general principles of perception, and on how our own perceptual systems work. This thesis describes machine listening research designed to solve real-world problems in perceptual and context-aware computing.;This thesis makes two claims. First, it claims that machine listening technologies are well-suited to the task of providing context awareness in real-world computational systems, whether these systems are intended to provide operational cues to smart devices or spaces, or to segment, summarize, or select segments of interest in multimedia corpora to make them more useful to human users. Second, it claims that the use of the core principle of perception, redundancy reduction, can guide the design of practical systems to provide context awareness in this way. The validity of these claims is supported by evidence from three application areas: multimedia gisting, acoustic environment recognition, and estimation of user interruptibility for the CHIL Connector service, a smart mobile telephone.
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