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Acoustic environment as an indicator of social and physical context

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Abstract

Acoustic environments provide many valuable cues for context-aware computing applications. From the acoustic environment we can infer the types of activity, communication modes and other actors involved in the activity. Environmental or background noise can be classified with a high degree of accuracy using recordings from microphones commonly found in PDAs and other consumer devices. We describe an acoustic environment recognition system incorporating an adaptive learning mechanism and its use in a noise tracker. We show how this information is exploited in a mobile context framework. To illustrate our approach we describe a context-aware multimodal weather forecasting service, which accepts spoken or written queries and presents forecast information in several forms, including email, voice and sign languages.

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Notes

  1. http://www.cmp.uea.ac.uk/research/noise_db

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Acknowledgements

We wish to thank, first, the VisiCast project team at UEA, particularly Judy Tryggvason, for the signing output, and Mike Lincoln for discussions on the language model, second, Steve Dorling and others from WeatherQuest, and, third, the anonymous reviewers whose comments have been much appreciated in revising this paper.

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Authors and Affiliations

  1. School of Computing Sciences, University of East Anglia, NR4 7TJ, Norwich, UK

    Dan Smith & Ling Ma

  2. Computing Laboratory, University of Kent, CT2 7NF, Canterbury, Kent, UK

    Nick Ryan

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  1. Dan Smith
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Correspondence to Dan Smith.

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Smith, D., Ma, L. & Ryan, N. Acoustic environment as an indicator of social and physical context. Pers Ubiquit Comput 10, 241–254 (2006). https://doi.org/10.1007/s00779-005-0045-4

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