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Robust Speech Enhancement Based on NPHMM Under Unknown Noise

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Nonlinear Speech Modeling and Applications (NN 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3445))

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Abstract

In this paper, a new speech enhancement based on the nonlinear H ∞  filtering and neural predictive HMM (NPHMM) is presented. In H ∞  filtering, no a priorknowledge of the noise source statistics is required. Speech is modeled as the output of a neural predictive HMM combining MLP neural network and HMM. The proposed enhancement method consists of multiple nonlinear H ∞  filters with parameter of the NPHMM. The switching between the nonlinear H ∞  filters is governed by a finite state Markov chain according to the transition probabilities. An approximate improvement of 0.4-1.8dB in output SNR is achieved at various input SNRs compared with conventional Kalman method.

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References

  1. Ephraim, Y.: A Bayesian estimation approach for speech enhancement using HMM. IEEE Trans. Signal Proc. 41, 725–735 (1993)

    Google Scholar 

  2. Lee, K.Y., et al.: Speech enhancement based on neural predictive hidden Markov model. Signal Processing 65, 373–381 (1998)

    Article  MATH  Google Scholar 

  3. Shen, X., Deng, L.: A dynamic system approach to speech enhancement using the H∞ filtering algorithm. IEEE Trans. Speech and Audio Proc. 7, 391–399 (1999)

    Article  Google Scholar 

  4. Banavar, R.N., Speyer, J.L.: A linear quadratic game theory approach to estimation and smoothing. In: Proc. IEEE ACC, pp. 2818–2822 (1991)

    Google Scholar 

  5. Nishiyama, K., Suzuki, K.: H∞ -learning of layered neural networks. IEEE Trans. Neural Networks 12, 1265–1277 (2001)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. School of Electronic Engineering, Soongsil University, Korea

    Ki Yong Lee

  2. School of Information and Technology, Korea Univ. of Technology and Education, Korea

    Jae Yeol Rheem

Authors
  1. Ki Yong Lee
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  2. Jae Yeol Rheem
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Editor information

Editors and Affiliations

  1. CNRS LTCI/TSI Paris, 46 rue Barrault, 75634, Paris Cedex 13, France

    Gérard Chollet

  2. Department of Psychology, Second University of Naples, and IIASS, Via Pellegrino 19, 84019, Vietri sul Mare, SA, Italy

    Anna Esposito

  3. Escola Universitària Politècnica de Mataró, Universitat Politècnica de Catalunya, Barcelona, Spain

    Marcos Faundez-Zanuy

  4. Dipartimento di Fisica “E.R. Caianiello”, Università degli Studi di Salerno, Via S. Allende, 84081, Baronissi, SA, Italy

    Maria Marinaro

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© 2005 Springer-Verlag Berlin Heidelberg

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Lee, K.Y., Rheem, J.Y. (2005). Robust Speech Enhancement Based on NPHMM Under Unknown Noise. In: Chollet, G., Esposito, A., Faundez-Zanuy, M., Marinaro, M. (eds) Nonlinear Speech Modeling and Applications. NN 2004. Lecture Notes in Computer Science(), vol 3445. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11520153_29

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  • DOI: https://doi.org/10.1007/11520153_29

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-27441-4

  • Online ISBN: 978-3-540-31886-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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