Use of Kalman discrete time and frequency filters combined with spectral power subtraction in the reduction of additive noise in speech signals contaminated by colored noise

Authors

DOI:

https://doi.org/10.33448/rsd-v11i7.30369

Keywords:

Kalman filter; Spectral power subtraction; Speech enrichment; Noise suppression.

Abstract

In This article aims to present and compare noise reduction techniques in the reconstruction of speech signals contaminated by colored noise in order to verify the spectral distortion generated by the processed signal. The discrete time and frequency Kalman filters in conjunction with the power spectral subtraction technique were used for noise reduction in signal reconstruction and compared. The signals used were contaminated by colored noise and the performance evaluation of the algorithms was performed using the segmented signal-to-noise ratio and the Itakura-Saito distance that verifies the spectral distortion in the processed signal. The best method found through the tests performed was the discrete time Kalman filter method together with the spectral power subtraction in relation to spectral distortion.

References

Berouti, M., Schwartz, R., & Makhoul, J. (1979, April). Enhancement of speech corrupted by acoustic noise. In ICASSP'79. IEEE International Conference on Acoustics, Speech, and Signal Processing (Vol. 4, pp. 208-211). IEEE.

Boll, S. (1979). Suppression of acoustic noise in speech using spectral subtraction. IEEE Transactions on acoustics, speech, and signal processing, 27(2), 113-120.

Brown, R. G., & Hwang, P. Y. (1997). Introduction to random signals and applied Kalman filtering: with MATLAB exercises and solutions. Introduction to random signals and applied Kalman filtering: with MATLAB exercises and solutions.

da Silva, L. A., Carrijo, G. A., Vasconcelos, E. S., Campos, R. D., Goulart, C. S., & Lemos, R. P. (2018). Comparative Study between the Discrete-Frequency Kalman Filtering and the Discrete-Time Kalman Filtering with Application in Noise Reduction in Speech Signals. Journal of Electrical and Computer Engineering, 2018.

da Silva, L. A., Gigo, W., Costa, S. S. D. O. J., Ferreira, J. C., & Júnior, A. C. L. (2021). A TRANSFORMADA WAVELET APLICADA NA REDUÇÃO DE RUÍDO EM SINAIS DE VOZ. JORNAL DE ENGENHARIA, TECNOLOGIA E MEIO AMBIENTE-JETMA, 6(2), 1-4.

Deller Jr, J. R. (1993). Discrete-time processing of speech signals. In Discrete-time processing of speech signals (pp. 908-908).

Fujimoto, M., & Ariki, Y. (2000, June). Noisy speech recognition using noise reduction method based on Kalman filter. In 2000 IEEE international conference on acoustics, speech, and signal processing. Proceedings (Cat. No. 00CH37100) (Vol. 3, pp. 1727-1730). IEEE.

Ma, N., Bouchard, M., & Goubran, R. A. (2004, May). Perceptual Kalman filtering for speech enhancement in colored noise. In 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing (Vol. 1, pp. I-717). IEEE.

Shao, Y., & Chang, C. H. (2006, May). A Kalman filter based on wavelet filter-bank and psychoacoustic modeling for speech enhancement. In 2006 IEEE International Symposium on Circuits and Systems (pp. 4-pp). IEEE.

Vaseghi, S. V. (2008). Advanced digital signal processing and noise reduction. John Wiley & Sons.

Published

01/06/2022

How to Cite

SILVA, L. A. da . Use of Kalman discrete time and frequency filters combined with spectral power subtraction in the reduction of additive noise in speech signals contaminated by colored noise. Research, Society and Development, [S. l.], v. 11, n. 7, p. e47211730369, 2022. DOI: 10.33448/rsd-v11i7.30369. Disponível em: https://rsdjournal.org/index.php/rsd/article/view/30369. Acesso em: 22 dec. 2024.

Issue

Section

Engineerings