Removal of Noise in Speech Signal – A Review

H. Hensiba*
Post Graduate, Department of Applied Electronics, C.S.I Institute of Technology, Thovalai, Tamil Nadu, India.
Periodicity:October - December'2017
DOI : https://doi.org/10.26634/jdp.5.4.14562

Abstract

In almost all the acoustic environments, noise is always considered as a ubiquitous one. The quality of the signal gets degraded and also contaminated because of the infection, which was caused by various sources when one speaks through the microphone. Here there is a possibility, where there may be a harm caused when human to machine communication happens. The digital filtering problem is considered in this paper, which is the estimation of the clean speech from Noise detection as well as Noise reduction. The estimation is done through linear filtering of noise in the speech signal. In this paper, the author has reviewed different and various speech signal processing techniques, where the noise gets affected and also how the noise gets removed.

Keywords

Noise Removal, Noise Detection, Speech, Signal Processing.

How to Cite this Article?

Hensiba, H. (2017). Removal of Noise in Speech Signal – A Review. i-manager's Journal on Digital Signal Processing, 5(4), 20-26. https://doi.org/10.26634/jdp.5.4.14562

References

[1]. AL-Allaf, O. N. (2015). Removing Noise from Speech Signals using different approaches of Artificial Neural Networks. I. J. Information Technology and Computer Science, 07, 8-18.
[2]. Alvarez, E., Mendez, R., & Langwagen, G. (2004, October). Detection of clicks using sinusoidal modeling for the confirmation of the clicks. In Proceedings of the 7 International Conference on Digital Audio Effects (DAFx04) (Vol. 8).
[3]. Anzalone, M. C., Calandruccio, L., Doherty, K. A., & Carney, L. H. (2006). Determination of the potential benefit of time-frequency gain manipulation. Ear and Hearing, 27(5), 480-492.
[4]. Arbogast, T. L., Mason, C. R., & Kidd Jr, G. (2002). The effect of spatial separation on informational and energetic masking of speech. The Journal of the Acoustical Society of America, 112(5), 2086-2098.
[5]. Brons, I., Houben, R., & Dreschler, W. A. (2012). Perceptual effects of noise reduction by time-frequency masking of noisy speech. The Journal of the Acoustical Society of America, 132(4), 2690-2699.
[6]. Brungart, D. S., Chang, P. S., Simpson, B. D., & Wang, D. (2006). Isolating the energetic component of speech-onspeech masking with ideal time-frequency segregation.
The Journal of the Acoustical Society of America, 120(6), 4007-4018.
[7]. Canazza, S., De Poli, G., & Mian, G. A. (2010). Restoration of audio documents by means of extended Kalman filter. IEEE Transactions on Audio, Speech, and Language Processing, 18(6), 1107-1115.
[8]. Cao, S., Li, L., & Wu, X. (2011). Improvement of intelligibility of ideal binary-masked noisy speech by adding background noise. The Journal of the Acoustical Society of America, 129(4), 2227-2236.
[9]. Cooke, M., Green, P., Josifovski, L., & Vizinho, A., (2001). Robust automatic speech recognition with missing and unreliable acoustic data. Speech Comm., 34, 267-285.
[10]. Garg, K., & Jain, G. (2016, September). A comparative study of noise reduction techniques for automatic speech recognition systems. In Advances in Computing, Communications and Informatics (ICACCI), 2016 International Conference on (pp. 2098-2103). IEEE.
[11]. Ma, N., Bouchard, M., & Goubran, R. A. (2006). Speech enhancement using a masking threshold constrained Kalman filter and its heuristic implementations. IEEE Transactions on Audio, Speech, and Language Processing, 14(1), 19-32.
[12]. Madhu, N., Spriet, A., Jansen, S., Koning, R., & Wouters, J. (2013). The potential for speech intelligibility improvement using the ideal binary mask and the ideal wiener filter in single channel noise reduction systems: Application to auditory prostheses. IEEE Transactions on Audio, Speech, and Language Processing, 21(1), 63-72.
[13]. Oudre, L. (2015). Automatic detection and removal of impulsive noise in audio signals. Image Processing on Line, 5, 267-281.
[14]. Rouat, J. (2008). Computational auditory scene analysis: Principles, algorithms, and applications (Wang, D. & Brown, G. J., Eds.; 2006) [Book Review]. IEEE Transactions on Neural Networks, 19(1), 199-199.
[15]. Saha, G., Chakroborty, S., & Senapati, S. (2005, January). A new silence removal and endpoint detection algorithm for speech and speaker recognition applications. In Proceedings of the 11 National Conference on Communications (NCC) (pp. 291-295).
[16]. Shirzadi, S., Kia, M, S, S., & Hashemi, S, T. (2016). Noise Removing of Audio Speech Signals by Means of Kalman Filter. International Journal of Advanced Biotechnology and Research (IJBR), 7, 98-103.
[17]. Wang, D. (2008). Time-frequency masking for speech separation and its potential for hearing aid design. Trends in Amplification, 12(4), 332-353.
[18]. Wang, D., Kjems, U., Pedersen, M. S., Boldt, J. B., & Lunner, T. (2009). Speech intelligibility in background noise with ideal binary time-frequency masking. The Journal of the Acoustical Society of America, 125(4), 2336-2347.
[19]. Wieland, B., Urban, K., & Funken, S. (2009). Speech Signal Noise Reduction with Wavelets (Doctoral Dissertation, Verlag nicht ermittelbar).
[20]. Zhang, X., & Xiong, Y. (2009). Impulse noise removal using directional difference based noise detector and Adaptive Weighted Mean Filter. IEEE Signal Processing Letters, 16(4), 295-298.
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