Skip to main content
SpringerLink
Log in

A hybrid frequency–time domain symmetric adaptive decorrelator

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

Symmetric adaptive decorrelation (SAD) is a semi-blind method of separating convolutely mixed signals. While it has restrictions on the physical layout of the demixing equipment, it is better suited for some applications (e.g., live sound mixing) as no post-processing is required to ascertain which output corresponds with which source. Since SAD is based on the least mean squares algorithm, it can be modified to perform the bulk of the processing in the frequency domain. This makes it more efficient for larger filter sizes and/or larger number of sources but renders it unsuitable for real-time applications as there is a lag between the output and the input. In this paper, we propose a hybrid approach that does not suffer from the lag of the frequency domain approach. While the proposed algorithm is slightly less computationally efferent than the pure frequency domain algorithm, it is significantly more efficient than the time domain approach. A comparison of the frequency domain and hybrid algorithms shows that both achieve separation equivalent to the time domain algorithm in a real-world environment.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Antoni, J., Chauhan, S.: A study and extension of second-order blind source separation to operational modal analysis. J. Sound Vib. 332(4), 1079–1106 (2013). doi:10.1016/j.jsv.2012.09.016

    Article  Google Scholar 

  2. Buchner, H., Aichner, R., Kellermann, W.: TRINICON: A versatile framework for multichannel blind signal processing. In: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP’04, vol. 3, pp. iii–889 (2004)

  3. Cardoso, J.F.: Blind signal separation: statistical principles. Proc. IEEE 86(10), 2009–2025 (1998). doi:10.1109/5.720250

    Article  Google Scholar 

  4. Cichocki, A., Amari, Si: Adaptive Blind Signal and Image Processing. Wiley, New York (2002)

    Book  Google Scholar 

  5. Cox, J.: The maximum tolerable delay of speech and music. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP ’85, vol. 10, pp. 612–615 (1985). doi:10.1109/ICASSP.1985.1168334

  6. Gardner, W.G.: Efficient convolution without input/output delay. In: Audio Engineering Society Convention 97. Audio Engineering Society (1994)

  7. Jafar, S.: Blind interference alignment. IEEE J. Sel. Top. Signal Process. 6(3), 216–227 (2012)

    Article  MathSciNet  Google Scholar 

  8. Li, X.L., Adal, T., Anderson, M.: Joint blind source separation by generalized joint diagonalization of cumulant matrices. Sig. Process. 91(10), 2314–2322 (2011). doi:10.1016/j.sigpro.2011.04.016

    Article  MATH  Google Scholar 

  9. Mehra, R., Raghuvanshi, N., Savioja, L., Lin, M.C., Manocha, D.: An efficient GPU-based time domain solver for the acoustic wave equation. Appl. Acoust. 73(2), 83–94 (2012). doi:10.1016/j.apacoust.2011.05.012

    Article  Google Scholar 

  10. Mirchandani, G., Zinser, R., Evans, J.: A new adaptive noise cancellation scheme in the presence of crosstalk [speech signals]. IEEE Trans. Circuits Syst. II Analog Digit. Signal Process. 39(10), 681–694 (1992). doi:10.1109/82.199895

    Article  MATH  Google Scholar 

  11. Pedersen, M.S., Wang, D., Larsen, J., Kjems, U.: Two-microphone separation of speech mixtures. IEEE Trans. Neural Netw. 19(3), 475–492 (2008). doi:10.1109/TNN.2007.911740

    Article  Google Scholar 

  12. Poh, M.Z., McDuff, D.J., Picard, R.W.: Non-contact, automated cardiac pulse measurements using video imaging and blind source separation. Opt. Express 18(10), 10762–10774 (2010)

    Article  Google Scholar 

  13. Poree, F., Kachenoura, A., Gauvrit, H., Morvan, C., Carrault, G., Senhadji, L.: Blind source separation for ambulatory sleep recording. IEEE Trans. Inf. Technol. Biomed. 10(2), 293–301 (2006). doi:10.1109/TITB.2005.859878

    Article  Google Scholar 

  14. Sawada, H., Mukai, R., Araki, S., Makino, S.: A robust and precise method for solving the permutation problem of frequency-domain blind source separation. IEEE Trans. Speech Audio Process. 12(5), 530–538 (2004). doi:10.1109/TSA.2004.832994

  15. Shynk, J.: Frequency-domain and multirate adaptive filtering. Signal Process. Mag. IEEE 9(1), 14–37 (1992). doi:10.1109/79.109205

    Article  Google Scholar 

  16. Torkkola, K.: Blind separation for audio signals are we there yet. In: Proceedings of International Workshop on Independent Component Analysis and Blind Separation of Signals (ICA 99), pp. 239–244 (1999)

  17. Tsalaile, T., Sameni, R., Sanei, S., Jutten, C., Chambers, J.: Sequential blind source extraction for quasi-periodic signals with time-varying period. IEEE Trans. Biomed. Eng. 56(3), 646–655 (2009). doi:10.1109/TBME.2008.2002141

    Article  Google Scholar 

  18. Tu, C.C., Champagne, B.: Subspace-based blind channel estimation for MIMO–OFDM systems with reduced time averaging. IEEE Trans. Veh. Technol. 59(3), 1539–1544 (2010). doi:10.1109/TVT.2009.2039008

    Article  Google Scholar 

  19. Van Gerven, S., Van Compernolle, D.: Signal separation by symmetric adaptive decorrelation: stability, convergence, and uniqueness. IEEE Trans. Signal Process. 43(7), 1602–1612 (1995). doi:10.1109/78.398721

    Article  Google Scholar 

  20. Waldmann, I.P., Tinetti, G., Deroo, P., Hollis, M.D.J., Yurchenko, S.N., Tennyson, J.: Blind extraction of an exoplanetary spectrum through independent component analysis. Astrophys. J. 766(1), 7 (2013). doi:10.1088/0004-637X/766/1/7

    Article  Google Scholar 

  21. Yoshioka, T., Nakatani, T., Miyoshi, M., Okuno, H.G.: Blind separation and dereverberation of speech mixtures by joint optimization. IEEE Trans. Audio Speech Lang. Process. 19(1), 69–84 (2011). doi:10.1109/TASL.2010.2045183

    Article  Google Scholar 

  22. Zhang, H., Li, L., Li, W.: Independent vector analysis for convolutive blind noncircular source separation. Signal Process. 92(9), 2275–2283 (2012). doi:10.1016/j.sigpro.2012.02.020

    Article  Google Scholar 

  23. Zinser, R., Mirchandani, G., Evans, J.: Some experimental and theoretical results using a new adaptive filter structure for noise cancellation in the presence of crosstalk. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP ’85, vol. 10, pp. 1253–1256 (1985)

Download references

Author information

Authors and Affiliations

  1. School of Engineering and Advanced Technology, Massey University, Albany, Auckland, New Zealand

    J. I. Harris & F. Alam

  2. School of Engineering, AUT University, Auckland, New Zealand

    T. J. Moir

Authors
  1. J. I. Harris
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. F. Alam
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. T. J. Moir
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to J. I. Harris.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Harris, J.I., Alam, F. & Moir, T.J. A hybrid frequency–time domain symmetric adaptive decorrelator. SIViP 11, 921–928 (2017). https://doi.org/10.1007/s11760-016-1040-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-016-1040-0

Keywords

Search

Navigation