A study of measures for noise reduction to improve the quality of speech using adjustment Kalman filter

##plugins.themes.academic_pro.article.main##

Author

  • Duong Ngoc Phap

Keywords:

Kalman filter
noise reduction
speech
speech presence probability
regression

Abstract

The article focuses on noise reduction techniques for speech using the adjusmtent Kalman filter. The construction of the regression model with matrix of estimated coefficients are made for both speech and noise to change the structure of the filter. Original speech affected by noise is processed prior to the input filter using the signal estimation method including the determination of the speech presence probability (SPP) and spectral subtraction technique. The simulation results are performed on the NOIZEUS database to simulate the studied technique with different kinds of noise and different noise levels. Through the evaluation criteria, the article compares the technical efficiency on noise environments and the methods studied previously.

References

    [1] Benesty, Jacob. Springer handbook of speech processing. Springer Science & Business Media, 2008.
    [2] Gerkmann, Timo, and Richard C. Hendriks. “Noise power estimation based on the probability of speech presence.” Applications of Signal Processing to Audio and Acoustics (WASPAA), 2011 IEEE Workshop on. IEEE, 2011.
    [3] Hu, Yi, and Philipos C. Loizou. “Evaluation of objective quality measures for speech enhancement.” Audio, Speech, and Language Processing, IEEE Transactions on 16.1 (2008): 229-238.
    [4] Paliwal, K., and A. Basu. “A Speech Enhancement Method Based on Kalman Filtering, proceedings of IEEE Int.” Conf. Acoust. Speech. 1987.
    [5] Martin, Rainer. “Noise power spectral density estimation based on optimal smoothing and minimum statistics.” Speech and Audio Processing, IEEE Transactions on 9.5 (2001): 504-512.
    [6] Martin, Rainer. “Spectral subtraction based on minimum statistics.” power 6 (1994): 8.
    [7] Berouti, M., R. Schwartz, and John Makhoul. “Enhancement of speech corrupted by acoustic noise.” Acoustics, Speech, and Signal Processing, IEEE International Conference on ICASSP'79.. Vol. 4. IEEE, 1979.
    [8] Paliwal, Kuldip, Kamil Wójcicki, and Belinda Schwerin. “Single-channel speech enhancement using spectral subtraction in the short-time modulation domain.” Speech communication 52.5 (2010): 450-475.
    [9] Popescu, Dimitrie C., and Ilija Zeljković. “Kalman filtering of colored noise for speech enhancement.” Acoustics, Speech and Signal Processing, 1998. Proceedings of the 1998 IEEE International Conference on. Vol. 2. IEEE, 1998.
    [10] Recommendation, I. T. U. T. “Perceptual evaluation of speech quality (PESQ), an objective method for end-to-end speech quality assessment of narrowband telephone networks and speech codecs.” ITU-T Recommendation (2001): 862.
    [11] So, Stephen, Kamil K. Wójcicki, and Kuldip K. Paliwal. “Single-channel speech enhancement using kalman filtering in the modulation domain.” INTERSPEECH. 2010.
    [12] http://ecs.utdallas.edu/loizou/speech/noizeus/ truy cập lần cuối 20/05/2015.
Read more

##plugins.themes.academic_pro.article.sidebar##

Published

Dec 31, 2015
Download
PDF (Tiếng Việt)
How to Cite
Duong Ngoc Phap. “A Study of Measures for Noise Reduction to Improve the Quality of Speech Using Adjustment Kalman Filter”. The University of Danang - Journal of Science and Technology, vol. 12, no. 97.2, Dec. 2015, pp. 45-48, https://jst-ud.vn/jst-ud/article/view/3072.

##plugins.themes.academic_pro.article.details##