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Artificial Brain and OfficeMateTR based on Brain Information Processing Mechanism

  • Soo-Young Lee
Part of the Studies in Computational Intelligence book series (SCI, volume 63)

Summary

The Korean Brain Neuroinformatics Research Program has dual goals, i.e., to understand the information processing mechanism in the brain and to develop intelligent machine based on the mechanism. The basic form of the intelligent machine is called Artificial Brain, which is capable of conducting essential human functions such as vision, auditory, inference, and emergent behavior. By the proactive learning from human and environments the Artificial Brain may develop oneself to become more sophisticated entity. The OfficeMate will be the first demonstration of these intelligent entities, and will help human workers at offices for scheduling, telephone reception, document preparation, etc. The research scopes for the Artificial Brain and OfficeMate are presented with some recent results.

Keywords

Independent Component Analysis Speech Recognition Auditory Cortex Independent Component Analysis Independent Component Analysis Algorithm 
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.

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References

  1. [1]
    Lee, S.Y.: Korean Brain Neuroinformatics Research Program: The 3rd Phase. International Joint Conference on Neural Networks, Budapest, Hungary (2004).Google Scholar
  2. [2]
    Itti L., Koch, C.: Computational model of visual attention. Nature Reviews Neuroscience 2 (2001) 194-203.CrossRefGoogle Scholar
  3. [3]
    Haxby, J.V., Hoffman, E.A., Gobbini, M.I.: The distributed human neural system for face perception. Trends in Cognitive Sciences 4 (2000) 223-233.CrossRefGoogle Scholar
  4. [4]
    Jeong, S.Y., Lee, S.Y.: Adaptive learning algorithm to incorporate additional functional constraints into neural networks. Neurocomputing 35(2000)73-90.zbMATHCrossRefGoogle Scholar
  5. [5]
    Olshausen, B., Field, D.: Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381 (1996) 607-609. CrossRefGoogle Scholar
  6. [6]
    Clement, R.S., Witte, R.S., Rousche, P.J., Kipke, D.R.: Functional connectivity in auditory cortex using chronic, multichannel unit recordings. Neurocomputing 26 (1999) 347-354. CrossRefGoogle Scholar
  7. [7]
    Lee, J.H., Lee, T.W., Jung, H.Y., Lee, S.Y.: On the Efficient Speech Feature Extraction Based on Independent Component Analysis. Neural Processing Letters 15 (2002) 235-245. zbMATHCrossRefGoogle Scholar
  8. [8]
    Hyvarinen, A., Hoyer, P.O., Inki, M.: Topographic independent component analysis. Neural Computation 13 (2001) 1527-1558.CrossRefGoogle Scholar
  9. [9]
    Jeon, H.B., Lee, J.H., Lee, S.Y.: On the center-frequency ordered speech feature extraction based on independent component analysis. International Conference on Neural Information Processing, Shanghai, China (2001)1199-1203.Google Scholar
  10. [10]
    Kim, T., Lee, S.Y.: Learning self-organized topology-preserving complex speech features at primary auditory cortex. Neurocomputing 65-66 (2005) 793-800. CrossRefGoogle Scholar
  11. [11]
    Eggermont, J.J.: Between sound and perception: reviewing the search for a neural code. Hearing Research 157 (2001) 1-42. CrossRefGoogle Scholar
  12. [12]
    Park, K.Y., Lee, S.Y.: An engineering model of the masking for the noiserobust speech recognition. Neurocomputing 52-54 (2003) 615-620.CrossRefGoogle Scholar
  13. [13]
    Yost, W.A.: Fundamentals of hearing - An introduction. Academic Press (2000).Google Scholar
  14. [14]
    Torkkola, T.: Blind separation of convolved sources based on information maximization. In Proc. IEEE Workshop on Neural Networks for Signal Processing, Kyoto (1996) 423-432.Google Scholar
  15. [15]
    Park, H.M., Jeong, H.Y., Lee, T.W., Lee, S.Y.: Subband-based blind signal separation for noisy speech recognition. Electronics Letters 35 (1999) 2011-2012. CrossRefGoogle Scholar
  16. [16]
    Dhir, C.S., Park, H.M., Lee, S.Y.: Permutation Correction of Filter Bank ICA Using Static Channel Characteristics. Proc. International Conf. Neural Information Processing, Calcutta, India (2004) 1076-1081.Google Scholar
  17. [17]
    Lee, S.Y., Mozer, M.C.: Robust Recognition of Noisy and Superimposed Patterns via Selective Attention. Neural Information Processing Systems 12 (1999) MIT Press 31-37. Google Scholar
  18. [18]
    Park, K.Y., and Lee, S.Y.: Out-of-Vocabulary Rejection based on Selective Attention Model. Neural Processing Letters 12 (2000) 41-48.zbMATHCrossRefGoogle Scholar
  19. [19]
    Kim, B.T., and Lee, S.Y.: Sequential Recognition of Superimposed Patterns with Top-Down Selective Attention. Neurocomputing 58-60 (2004) 633-640.CrossRefGoogle Scholar
  20. [20]
    Bae, U.M., Park, H.M., Lee, S.Y.: Top-Down Attention to Complement Independent Component Analysis for Blind Signal Separation. Neuro-computing 49 (2002) 315-327. Google Scholar
  21. [21]
    Lee, M., and Lee, S.Y.: Unsupervised Extraction of Multi-Frame Features for Lip-Reading. Neural Information Processing - Letters and Reviews 10 (2006)97-104.Google Scholar
  22. [22]
    Kim, C.M., Park, H.M., Kim, T., Lee, S.Y., Choi, Y.K.: FPGA Implementation of ICA Algorithm for Blind Signal Separation and Active Noise Canceling. IEEE Transactions on Neural Networks 14 (2003) 1038-1046.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Soo-Young Lee
    • 1
  1. 1.Korea Advanced Institute of Science and TechnologyKorea

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