Constructing Time-Frequency Dictionaries for Source Separation via Time-Frequency Masking and Source Localisation

  • Ruairí de Fréin
  • Scott T. Rickard
  • Barak A. Pearlmutter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5441)


We describe a new localisation and source separation algorithm which is based upon the accurate construction of time-frequency spatial signatures. We present a technique for constructing time-frequency spatial signatures with the required accuracy. This algorithm for multi-channel source separation and localisation allows arbitrary placement of microphones yet achieves good performance. We demonstrate the efficacy of the technique using source location estimates and compare estimated time-frequency masks with the ideal 0 dB mask.


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  1. 1.
    Malioutov, D.M., Çetin, M., Willsky, A.S.: A sparse signal reconstruction perspective for source localization with sensor arrays. IEEE Transactions on Signal Processing 53, 3010–3022 (2005)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Model, D., Zibulevsky, M.: Signal Reconstruction in Sensor Arrays using Sparse Representations. Signal Processing 86, 624–638 (2006)CrossRefMATHGoogle Scholar
  3. 3.
    Gorodnitsky, I.F., Rao, B.D.: Sparse signal reconstruction from limited data using FOCUSS: A re-weighted minimum norm algorithm. IEEE Transactions on Signal Processing 45, 600–616 (1997)CrossRefGoogle Scholar
  4. 4.
    Zibulevsky, M., Pearlmutter, B.A.: Blind source separation by sparse decomposition in a signal dictionary. Neural Computation 13, 863–882 (2001)CrossRefMATHGoogle Scholar
  5. 5.
    Reddy, A.M., Raj, B.: Soft Mask Methods for Single-Channel Speaker Separation. IEEE Transactions on Audio, Speech, and Language Processing 15, 1766–1776 (2007)CrossRefGoogle Scholar
  6. 6.
    de Frein, R., Drakakis, K., Rickard, S.T.: Portfolio diversification using subspace factorizations. In: Proceedings of the Annual Conference on Information Sciences and Systems, pp. 1075–1080 (2008)Google Scholar
  7. 7.
    Rickard, S.T.: Sparse sources are separated sources. In: Proceedings of the 16th Annual European Signal Processing Conference, Florence, Italy (2006)Google Scholar
  8. 8.
    Yilmaz, O., Rickard, S.T.: Blind separation of speech mixtures via time-frequency masking. IEEE Transactions on Signal Processing 52, 1830–1847 (2004)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Hoyer, P.O.: Non-negative sparse coding. In: Proceedings of the IEEE Workshop on Neural Networks for Signal Processing, pp. 557–565 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Ruairí de Fréin
    • 1
  • Scott T. Rickard
    • 1
  • Barak A. Pearlmutter
    • 2
  1. 1.Complex & Adaptive Systems LaboratoryUniversity College DublinIreland
  2. 2.Hamilton InstituteNational University of Ireland MaynoothCo. KildareIreland

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