The Journal of the Acoustical Society of Korea

The Acoustical Society of Korea (한국음향학회)
권호 표지

Speech Enhancement Using Blind Signal Separation Combined With Null Beamforming

  • Nam Seung-Hyon (Department of Electronic Engineering, Paichai University) ;
  • Jr. Rodrigo C. Munoz (Institute of Engineering and Architecture, Bataan Polytechnic State College, Philippines)
  • Published : 2006.12.30
Download PDF
⟨ Previous Next ⟩

Abstract

Blind signal separation is known as a powerful tool for enhancing noisy speech in many real world environments. In this paper, it is demonstrated that the performance of blind signal separation can be further improved by combining with a null beamformer (NBF). Cascading the blind source separation with null beamforming is equivalent to the decomposition of the received signals into the direct parts and reverberant parts. Investigation of beam patterns of the null beamformer and blind signal separation reveals that directional null of NBF reduces mainly direct parts of the unwanted signals whereas blind signal separation reduces reverberant parts. Further, it is shown that the decomposition of received signals can be exploited to solve the local stability problem. Therefore, faster and improved separation can be obtained by removing the direct parts first by null beamforming. Simulation results using real office recordings confirm the expectation.

Keywords

References

  1. S.-I. Amari, S. C. Douglas, A. Cichocki, and H. H. Yang, 'Novel online adaptive learning algorithrns for blind deconvolution using the natural gradient approach,' Proc. IEEE 11th IFAC Symposium on System Identification, SYSID-97, Kitakyushu, Japan, 1057-1062, 1997
  2. S. H. Nam and S. Beack, 'A frequency-domain nomalized multichannel deconvolutive algorithm for acoustical signals,' Proc. Int. Workshop on Independent Component Analysis and Blind Signal Separation (ICA'04), Granada, Spain, 522-529, 2004
  3. S. Beack, S. H. Nam, and M. Hahn, 'A new speech enhancement algorithm for car environment noise cancellation with MBD and Kalman filtering,' IEICE Trans. Fundamentals, vol E88-A, 3 685-689, March 2005 https://doi.org/10.1093/ietfec/e88-a.3.685
  4. Seung-Hyon Nam, 'A new formulation of multichannel blind deconvolution and its properties for speech enhancement,' Proc. of Western Pacific Acoustics Conf., Seoul, Korea, June 2006
  5. C. Fancourt and L. Parra, 'The generalized sidelobe decorrelator,' IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, 167-170, Oct. 2001
  6. H. Saruwatari, T. Kawamura, K. Sawai, A. Kaminuma, M. Sakata, 'Blind source separation based on fast-convergence algorithm using ICA and beamforming for real convolutive mixture,' Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, 3097-3100, May 2002,
  7. H. Saruwatari, S. Kurita, K. Takeda, F. Itakura, and K. Shikano, 'Blind source separation based on subband ICA and beamforming,' Proc. ICSLP2000, 3 99-97, Oct. 2000
  8. R. Aichner, S. Araki, S. Makino, T. Nishikawa, and H. Saruwatari, 'Timedomain blind source separation of nonstationary convolutive signals by utilizing geometry beamforming,' Proc. IEEE Workshop on NNSP' 02, 445-454, 2002
  9. R. Aichner, H. Buchner, and W. Kellermann, 'On the causality problem in the time-domain blind source separation and deconvolution algorithms,' Proc. ICASSP, 2005
  10. R. Mukai, S. Araki, and S. Makino, 'Separation and dereverberation performance of frequency domain blind source separation,' Proc. lnt. Workshop on Independent Component Analysis and Blind Signal Separation, 230-235, Dec. 2001,
  11. J. Bourgeois, 'An LMS viewpoint on the local stability of second order blind source separation,' in Proc. IEEE Workshop on Statistical Signal Processing, Bordeaux, France, 1995, 49-53,