DOI QR코드

DOI QR Code

Research on Noise Reduction Algorithm Based on Combination of LMS Filter and Spectral Subtraction

  • Cao, Danyang (School of Computer, North China University of Technology) ;
  • Chen, Zhixin (School of Computer, North China University of Technology) ;
  • Gao, Xue (School of Computer, North China University of Technology)
  • Received : 2017.11.06
  • Accepted : 2018.10.17
  • Published : 2019.08.31

Abstract

In order to deal with the filtering delay problem of least mean square adaptive filter noise reduction algorithm and music noise problem of spectral subtraction algorithm during the speech signal processing, we combine these two algorithms and propose one novel noise reduction method, showing a strong performance on par or even better than state of the art methods. We first use the least mean square algorithm to reduce the average intensity of noise, and then add spectral subtraction algorithm to reduce remaining noise again. Experiments prove that using the spectral subtraction again after the least mean square adaptive filter algorithm overcomes shortcomings which come from the former two algorithms. Also the novel method increases the signal-to-noise ratio of original speech data and improves the final noise reduction performance.

Keywords

References

  1. X. Wang, L. Li, and C. Liu, "Study of speech enhancement algorithm based on spectral subtraction," Journal of the Staff and Worker's University, vol. 2013, no. 6, pp. 85-87, 2013.
  2. S. Boll, "Suppression of acoustic noise in speech using spectral subtraction," IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 27, no. 2, pp. 113-120, 1979. https://doi.org/10.1109/TASSP.1979.1163209
  3. D. Tsoukalas, M. Paraskevas, and J. Mourjopoulos, "Speech enhancement using psychoacoustic criteria," in Proceedings of 1993 IEEE International Conference on Acoustics, Speech, and Signal Processing, Minneapolis, MN, 1993, pp. 359-362.
  4. J. S. Lim and A. V. Oppenheim, "Enhancement and bandwidth compression of noisy speech," Proceedings of the IEEE, vol. 67, no. 12, pp. 1586-1604, 1979. https://doi.org/10.1109/PROC.1979.11540
  5. B. Widrow and M. E. Hoff, "Adaptive switching circuits," Stanford Electronics Labs, Stanford University, CA, Report No. TR-1553-1, 1960.
  6. D. L. Donoho and J. M. Johnstone, "Ideal spatial adaptation by wavelet shrinkage," Biometrika, vol. 81, no. 3, pp. 425-455, 1994. https://doi.org/10.1093/biomet/81.3.425
  7. S. Thomas Alexander, Adaptive Signal Processing: Theory and Applications. New York, NY: Springer, 1986.
  8. W. Xu, G. Wang, Y. Geng, F. Bai, and T. Fei, "Speech enhancement algorithm based on spectral subtraction and variable-step LMS algorithm," Computer Engineering and Applications, vol. 51, no. 1, pp. 213-217, 2015.
  9. H. Chen and X. H. Qiu, "Research on speech enhancement of improved spectral subtraction algorithm," Computer Technology and Development, vol. 24, no. 4, pp. 70-76, 2014.
  10. A. D. Poularikas and Z. M. Ramadan, Adaptive Filtering Primer with MATLAB. Boca Raton, FL: CRC Press, 2006.
  11. Z. Song, The Application of MATLAB in Speech Signal Analysis and Synthesis. Beijing: Beihang University Press, 2013.
  12. J. Han, C. Wang, C. Lu, L. Zhang, W Ren, and Y. Ma, "Robust speech recognition system in noisy environment," Audio Engineering, vol. 2002, no. 1, pp. 27-29, 2002.
  13. J. Bai, L. H. Yang, and X. Y. Zhang, "An antinoise SVM parameter optimization method for speech recognition," Journal of Central South University: Science and Technology, vol. 44, no. 2, pp. 604-611, 2013.
  14. R. Wang and P. Chai, "A method for speech enhancement based on improved spectral subtraction," Pattern Recognition and Artificial Intelligence, vol. 16, no. 2, pp. 247-251, 2003. https://doi.org/10.3969/j.issn.1003-6059.2003.02.022
  15. Y. Yang and W. Shi, "Implementation of adaptive filter on wave-generated magnetic noise based on LMS algorithm," Journal of Jiangsu Institute of Education (Natural Science), vol. 27, no. 1, pp. 9-10, 2011.