Skip to main content
SpringerLink
Log in

Blind separation of audio signals using trigonometric transforms and Kalman filtering

  • Published:
International Journal of Speech Technology Aims and scope Submit manuscript

Abstract

This paper deals with the problem of blind separation of audio signals from noisy mixtures. It proposes the application of a blind separation algorithm on the Discrete Cosine Transform (DCT) or the Discrete Sine Transform (DST) of the mixed signals, instead of performing the separation on the mixtures in the time domain. Kalman Filtering of the noisy separated signals is recommended in this paper as a post-processing step for noise reduction. Both the DCT and the DST have an energy compaction property, which concentrates most of the signal energy in a few coefficients in the transform domain, leaving the rest of the transform-domain coefficients close to zero. As a result, the separation is performed on a few coefficients in the transform domain. Another advantage of signal separation in transform domains is that the effect of noise on the signals in the transform domains is smaller than that in the time domain due to the averaging effect of the transform equations. The simulation results confirm the effectiveness of transform-domain signal separation and the feasibility of the post-processing Kalman filtering step.

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.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  • Bozic, S. M. (1994). Digital and Kalman filtering (2Rev ed.). British library cataloguing in publication data.

  • Chan, D. C. (1997). Blind Signal Separation. A PhD dissertation, University of Cambridge, January.

  • Crochiere, R. E., Tribolet, J. E., & Rabiner, L. R. (1980). An interpretation of the log likelihood ratio as a measure of waveform coder performance. IEEE Transactions on Acoustics, Speech, and Signal Processing, Assp-28(3), 318–323.

    Article  Google Scholar 

  • Curnew, S. R., & How, J. (2007). Blind signal separation in MIMO OFDM systems using ICA and fractional sampling. In Signals, systems and electronics, 2007. ISSSE ’07. International symposium (pp. 67–70).

    Chapter  Google Scholar 

  • Dam, H. H., Nordholm, S., Low, S. Y., & Cantoni, A. (2007). Blind signal separation using steepest descent method. IEEE Transactions on Signal Processing, 55(8), 4198–4207.

    Article  MathSciNet  Google Scholar 

  • Hammam, H., Abu El-Azm, A. E., Elhalawany, M. E., & Abd El-Samie, F. E. (2010). Blind separation of audio signals using trigonometric transforms and wavelet denoising. Int. J. Speech Technol., 13(1), 1–12. doi:10.1007/s10772-010-9066-0.

    Article  Google Scholar 

  • Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Trans. ASME J. of Basic Engineering, 35–45.

  • Kubichek, R. (1993). Mel-cepstral distance measure for objective speech quality assessment. In Proceedings of the IEEE pacific Rim conference on communications, computers and signal processing (pp. 125–128).

    Chapter  Google Scholar 

  • Manmontri, U., & Naylor, P. A. (2008). A class of Frobenius norm-based algorithms using penalty term and natural gradient for blind signal separation. IEEE Transactions on Audio, Speech, and Language Processing, 16(6), 1181–1193.

    Article  Google Scholar 

  • Moreau, M., Pesquet, J. C., & Thirion-Moreau, N. (2007). Convolutive blind signal separation based on asymmetrical contrast functions. IEEE Transactions on Signal Processing, 55(1), 356–371.

    Article  MathSciNet  Google Scholar 

  • Prochazka, A., Uhlir, J., Rayner, P. J. W., & Kingsbury, N. J. (1998). Signal analysis and prediction. Basel: Birkhauser.

    MATH  Google Scholar 

  • Sakai, Y., & Mitsuhashi, W. (2008). A study on the property of blind source separation for preprocessing of an acoustic echo cancellar. In SICE annual conference (pp. 13–18).

    Chapter  Google Scholar 

  • Szupiluk, R., Wojewnik, P., & Zabkowski, T. (2006). Blind signal separation methods for integration of neural networks results. In Information fusion, 9th international conference (pp. 1–6).

    Chapter  Google Scholar 

  • Walker, J. S. (1999). A primer on wavelets and their scientific applications. Boca Raton: CRC Press.

    Book  MATH  Google Scholar 

  • Wang, S., Sekey, A., & Gersho, A. (1992). An objective measure for predicting subjective quality of speech coders. IEEE Journal on Selected Areas in Communications, 10(5), 819–829.

    Article  Google Scholar 

  • Won, Y. G., & Lee, S. Y. (2008). Convolutive blind signal separation by estimating mixing channels in time domain. Electronics Letters, 44(21), 1277–1279.

    Article  Google Scholar 

  • Yang, W., Benbouchta, M., & Yantorno, R. (1998). Performance of the modified bark spectral distortion as an objective speech quality measure. In Proceedings of the IEEE international conf. on acoustic, speech and signal processing (ICASSP), Washington, USA (Vol. 1, pp. 541–544).

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electronics and Communications, Faculty of Engineering, University of Aden, Aden, Yemen

    Mussa M. Ahmed

  2. Department of Electronics and Electrical Communications, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt

    Fathi E. Abd El-Samie

Authors
  1. Mussa M. Ahmed
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fathi E. Abd El-Samie
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fathi E. Abd El-Samie.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Ahmed, M.M., Abd El-Samie, F.E. Blind separation of audio signals using trigonometric transforms and Kalman filtering. Int J Speech Technol 16, 7–17 (2013). https://doi.org/10.1007/s10772-012-9143-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10772-012-9143-7

Keywords

Search

Navigation