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Separation of Mixed Audio Signals by Source Localization and Binary Masking with Hilbert Spectrum

  • Conference paper
Independent Component Analysis and Blind Signal Separation (ICA 2006)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 3889))

Abstract

The Hilbert transformation together with empirical mode decomposition (EMD) produces Hilbert spectrum (HS) which is a fine-resolution time-frequency (TF) representation of any nonlinear and non-stationary signal. A method of audio signal separation from stereo mixtures based on the spatial location of the sources is presented in this paper. The TF representation of the audio signal is obtained by HS. The sources are localized in the space of time and intensity differences between two microphones’ signals. The separation is performed by masking the target signal in TF domain considering that the sources are disjoint orthogonal. The experimental results of the proposed method show a noticeable improvement of separation efficiency.

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References

  1. Yilmaz, O., Rickard, S.: Blind Separation of Speech Mixtures via Time-Frequency Masking. IEEE Transactions on Signal Processing 52(7), 1830–1847 (2004)

    Article  MathSciNet  Google Scholar 

  2. Roman, N., Wang, D., Brown, G.J.: Speech segregation based on sound localization. Acost. Soc. of America 114(4), 2236–2252 (2003)

    Article  Google Scholar 

  3. Baeck, M., Zolzer, U.: Real-Time Implementation of Source Separation Algorithm. DAFx- 03, London, UK (2003)

    Google Scholar 

  4. http://sound.media.mit.edu/KEMAR.html

  5. Huang, N.E., et al.: The empirical mode decomposition and Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. Roy. Soc. London A 454, 903–995 (1998)

    Article  MATH  Google Scholar 

  6. Flandrin, P., Rilling, G., Goncalves, P.: Emperical Mode Decomposition as a filter bank. IEEE Sig. Proc. Letter (2003)

    Google Scholar 

  7. Wu, B.Z., Huang, N.E.: A study of the characteristics of white noise using the empirical mode decomposition method. Proc. R. Soc. Lond. A (460), 1597–1611 (2004)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Graduate School of Frontier Sciences,  

    Md. Khademul Islam Molla & Nabuaki Minematsu

  2. Graduate School of Information Science and Technology, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan

    Keikichi Hirose

Authors
  1. Md. Khademul Islam Molla
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  2. Keikichi Hirose
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  3. Nabuaki Minematsu
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Editor information

Editors and Affiliations

  1. Siemens Corporate Research, 755 College Road East, 08540, Princeton, NJ, USA

    Justinian Rosca

  2. Department of CSEE, Oregon Health and Science University, Portland, Oregon, USA

    Deniz Erdogmus

  3. Dep. of Electrical and Computer Engineering, University of Florida, Gainesville, Florida, USA

    José C. Príncipe

  4. McMaster University, 1280 Main Street West, L8S 4K1, Hamilton, Ontario, Canada

    Simon Haykin

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© 2006 Springer-Verlag Berlin Heidelberg

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Molla, M.K.I., Hirose, K., Minematsu, N. (2006). Separation of Mixed Audio Signals by Source Localization and Binary Masking with Hilbert Spectrum. In: Rosca, J., Erdogmus, D., Príncipe, J.C., Haykin, S. (eds) Independent Component Analysis and Blind Signal Separation. ICA 2006. Lecture Notes in Computer Science, vol 3889. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11679363_80

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  • DOI: https://doi.org/10.1007/11679363_80

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-32630-4

  • Online ISBN: 978-3-540-32631-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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