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A Self Tuning Fuzzy Inference System for Noise Reduction

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Book cover Artificial Intelligence and Soft Computing - ICAISC 2004 (ICAISC 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3070))

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Abstract

In this paper, a method for the reduction of noise in a speech signal is introduced. In the implementing of the method, firstly a high resolution frequency map of the signal is obtained. Each frequency band component of the signal is then segmented. Fuzzy inference system (FIS) is used for the determination of the noise contents of the segments. The output of the FIS is the suppression level of the segment. If the FIS decides that the segment contains only noise, then the segment is deleted or if the FIS decides that the segment is noise free, it is allowed to be passed without suppression. Since the signal to noise ratio (SNR) varies from case to case, the limits of the membership functions are tuned accordingly. This self tuning capability gives flexibility and robustness of the system.

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References

  1. Ephraim, Y.: Statistical-Model-Based Speech Enhancement Systems. Proc. Of the IEEE 80(10) (October 1992)

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  2. Hellendoorn, H., Reinfrank, M., Driankov, D.: An Introduction to Fuzzy Control. Springer, Heidelberg (1996)

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  3. Stearns, S.D.: Adaptive Signal Processing, Bernard Widrow. Prentice Hall, Englewood Cliffs (1985)

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  4. Vashegi, S.V.: Advanced Signal Processing and Digital Noise Reduction. Willey-Teubner (1996)

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  5. Duru, N.: Fuzzy Logic Based Noise Reduction System (in Turkish), Ph. D. Thesis, KOU Inst. of Science (1998)

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Author information

Authors and Affiliations

  1. Department of Computer Eng., University of Kocaeli, 41440, Kocaeli, Turkey

    Nevcihan Duru

  2. Department of Electrical Eng., University of Kocaeli, 41440, Kocaeli, Turkey

    Tarik Duru

Authors
  1. Nevcihan Duru
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  2. Tarik Duru
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Editor information

Editors and Affiliations

  1. Department of Artificial Intelligence, Academy of Humanities and Economics, Poland

    Leszek Rutkowski

  2. German Research Center of Artificial Intelligence (DFKI), Germany

    Jörg H. Siekmann

  3. Institute of Automatics, AGH University of Science and Technology, Al. Mickiewicza 30, PL-30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  4. Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Initiative in Soft Computing (BISC), 94720-1776, Berkeley, CA

    Lotfi A. Zadeh

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© 2004 Springer-Verlag Berlin Heidelberg

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Duru, N., Duru, T. (2004). A Self Tuning Fuzzy Inference System for Noise Reduction. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds) Artificial Intelligence and Soft Computing - ICAISC 2004. ICAISC 2004. Lecture Notes in Computer Science(), vol 3070. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24844-6_40

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  • DOI: https://doi.org/10.1007/978-3-540-24844-6_40

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22123-4

  • Online ISBN: 978-3-540-24844-6

  • eBook Packages: Springer Book Archive

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