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Intelligent Diagnostics for Sound Reproduction System by the Use of PEAQ

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Advances in Neural Networks - ISNN 2006 (ISNN 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3973))

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Abstract

After finishing the design of the sound reproduction system, it is necessary and important to evaluate how much each component of the sound reproduction system affects the final quality of audio. In this paper, we present an intelligent diagnostics for sound reproduction system. It is based on PEAQ(ITU-R BS. 1387) and artificial neural networks. This method makes it possible to consider the human preferences for various genres of music which differ from nation to nation and culture to culture.

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References

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Author information

Authors and Affiliations

  1. R&D Lab., NEX1 FUTURE Co., Ltd., 148-1 Mabuk Gusung, Yongin Kyunggi, 449-910, Korea

    Byung Doo Jun

  2. School of Electrical Engineering and Computer Science, Seoul National University, Shinlim-dong, Kwanak-gu, Seoul, 151-742, Korea

    Nakjin Choi, Hyun-Woo Ko & KoengMo Sung

Authors
  1. Byung Doo Jun
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  2. Nakjin Choi
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  3. Hyun-Woo Ko
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  4. KoengMo Sung
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Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

  3. Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A

    Jacek M. Zurada

  4. Laboratory for Computational Biology, Shanghai Center for Systems Biomedicine, 800 Dong Chuan Rd, 200240, Shanghai, China

    Bao-Liang Lu

  5. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

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© 2006 Springer-Verlag Berlin Heidelberg

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Jun, B.D., Choi, N., Ko, HW., Sung, K. (2006). Intelligent Diagnostics for Sound Reproduction System by the Use of PEAQ. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760191_57

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  • DOI: https://doi.org/10.1007/11760191_57

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34482-7

  • Online ISBN: 978-3-540-34483-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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