The CLEAR 2006 CMU Acoustic Environment Classification System

  • Robert G. Malkin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4122)


We describe the CLEAR 2006 acoustic environment classification evaluation and the CMU system used in the evaluation. Environment classification is a critical technology for the CHIL Connector service [1] in that Connector relies on maintaining awareness of user state to make intelligent decisions about the optimal times, places, and methods to deal with requests for human-to-human communication. Environment is an important aspect of user state with respect to this problem; humans may be more or less able to deal with voice or text communications depending on whether they are, for instance, in an office, a car, a cafe, or a cinema. We unfortunately cannot rely on the availability of the full CHIL sensor suite when users are not in the CHIL room; hence, we are motivated to explore the use of the only sensor which is reliably available on every mobile communication device: the microphone.


Independent Component Analysis Independent Component Analysis Optimal Code Acoustic Environment Babble Noise 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Danninger, M., Flaherty, G., Malkin, R., Steifelhagen, R., Waibel, A.: The Connector — facilitating context-aware communication. In: Proceedings of the International Conference on Multimodal Interfaces (2005)Google Scholar
  2. 2.
    Waibel, A., Steusloff, H., Stiefelhagen, R., The CHIL Project Consortium: CHIL: Computers in the human interaction loop. In: Proceedings of the International Workshop on Image Analysis for Multimedia Interactive Services (2004)Google Scholar
  3. 3.
    Casas, J., Stiefelhagen, R., et. al.: Multi-camera / multi-microphone system design for continuous room monitoring. CHIL Consortium Deliverable D4.1 CHIL-WP4-D4.1. The CHIL Consortium (2004)Google Scholar
  4. 4.
    Malkin, R., Waibel, A.: Classifying user environment for mobile applications using linear autoencoding of ambient audio. In: Proceedings of the International Conference on Acoustics, Speech, and Signal Processing (2005)Google Scholar
  5. 5.
    Barlow, H.B.: Possible principles underlying the transformation of sensory messages. In: Rosenbluth, W.A. (ed.) Sensory Communication, MIT Press, Cambridge (1961)Google Scholar
  6. 6.
    Atick, J.J.: Could information theory provide an ecological theory of sensory processing? In: Network: Computation in Neural Systems (1992)Google Scholar
  7. 7.
    Smaragdis, P.: Redundancy reduction for computational audition, a unifying approach, Ph.D. thesis, MIT (2001)Google Scholar
  8. 8.
    Duda, R., Hart, P., Stork, D.: Pattern Classification. John Wiley and Sons, Chichester (2001)zbMATHGoogle Scholar
  9. 9.
    Lewicki, M.S., Sejnowski, T.J.: Learning overcomplete representations. In: Neural Computation (2000)Google Scholar
  10. 10.
    Reyes-Gomez, M., Ellis, D.: Selection, parameter estimation, and discriminative training of hidden markov models for general audio modeling. In: Proceedings of the International Conference on Multimedia and Expo (2003)Google Scholar
  11. 11.
    Ellis, D., Lee, K.S.: Minimal-impact audio-based personal archives. Workshop on Continuous Archiving and Recording of Personal Experiences (2004)Google Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Robert G. Malkin
    • 1
  1. 1.interACT, Carnegie Mellon University, Pittsburgh PA 15213USA

Personalised recommendations