a b s t r a c tAutomatic sound recognition (ASR) has attracted increased and wide ranging interests in recent years. In this paper, we carry out a review of some important contributions in ASR techniques, mainly over the last one and a half decades. Similar to speech recognition systems, the robustness of an ASR system largely depends on the choice of feature(s) and classifier(s). We take a wider perspective in providing an overview of the features and classifiers used in ASR systems starting from early works in content-based audio classification to more recent developments in applications such as sound event recognition, audio surveillance, and environmental sound recognition. We also review techniques that have been utilized in noise robust sound recognition systems and feature optimization methods. Finally, some of the less commonly known applications of ASR are discussed.

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This paper explores the problems of speech recognition in a (sometimes) noisy environment. An adaptive acoustic beamformer is proposed based on the Griffiths-Jim method and a 'hot-spot' where speech can be received within a geometric-defined boundary and rejected outside of it will be shown to give a certain amount of noise immunity and improve the signal-tonoise ratio for the second stage, which is the speech recognition engine. The recognition engine used has a limited vocabulary which gives rise to an excellent hit-rate and less training than unlimited vocabulary. The technology here has improved vastly within the last decade and it will be shown that by using a head and shoulders avatar that is both photo-realistic and with appealing personality, the experience of a speech interface is vastly enhanced. The paper will explore these technologies and investigate the convergence of many of them in the current Massey smart-office.