Share This Article:

Noise Removal in Speech Processing Using Spectral Subtraction

Abstract Full-Text HTML XML Download Download as PDF (Size:430KB) PP. 32-41
DOI: 10.4236/jsip.2014.52006    7,770 Downloads   12,473 Views   Citations

ABSTRACT

Spectral subtraction is used in this research as a method to remove noise from noisy speech signals in the frequency domain. This method consists of computing the spectrum of the noisy speech using the Fast Fourier Transform (FFT) and subtracting the average magnitude of the noise spectrum from the noisy speech spectrum. We applied spectral subtraction to the speech signal “Real graph”. A digital audio recorder system embedded in a personal computer was used to sample the speech signal “Real graph” to which we digitally added vacuum cleaner noise. The noise removal algorithm was implemented using Matlab software by storing the noisy speech data into Hanning time-widowed half-overlapped data buffers, computing the corresponding spectrums using the FFT, removing the noise from the noisy speech, and reconstructing the speech back into the time domain using the inverse Fast Fourier Transform (IFFT). The performance of the algorithm was evaluated by calculating the Speech to Noise Ratio (SNR). Frame averaging was introduced as an optional technique that could improve the SNR. Seventeen different configurations with various lengths of the Hanning time windows, various degrees of data buffers overlapping, and various numbers of frames to be averaged were investigated in view of improving the SNR. Results showed that using one-fourth overlapped data buffers with 128 points Hanning windows and no frames averaging leads to the best performance in removing noise from the noisy speech.

Cite this paper

Karam, M. , Khazaal, H. , Aglan, H. and Cole, C. (2014) Noise Removal in Speech Processing Using Spectral Subtraction. Journal of Signal and Information Processing, 5, 32-41. doi: 10.4236/jsip.2014.52006.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1] Hymavathy, K.P. and Janardhanan, P. (2013) Noise Filtering in Speech Using Frequency Response Masking Technique. International Journal of Emerging Trends in Engineering and Development, 2.
[2] Muangjaroen, S. and Yingthawornsuk, T. (2012) A Study of Noise Reduction in Speech Signal Using FIR Filtering. Proceedings of the International Conference on Advances in Electrical and Electronics Engineering, Pattaya, 13-15 April 2012.
[3] Kumar, T.L. and Rajan, K.S. (2012) Noise Suppression in Speech Signals Using Adaptive Algorithms. International Journal of Engineering Research and Applications, 2, 718-721.
[4] Aggarwal, R., Singh, J.K., Gupta, V.K., Rathore, S., Tiwari, M. and Khare, A. (2011) Noise Reduction of Speech Signal Using Wavelet Transform with Modified Universal Threshold. International Journal of Computer Applications, 20, 15-19.
[5] Verteletskaya, E. and Simak, B. (2010) Speech Distortion Minimized Noise Reduction Algorithm. Proceedings of the World Congress on Engineering and Computer Science, Vol. I, San Francisco, 20-22 October 2010.
[6] Boll, S.F. (1979) Suppression of Acoustic Noise in Speech Using Spectral Subtraction. IEEE Transactions on Acoustic, Speech and Signal Processing, 27, 113-120.
http://dx.doi.org/10.1109/TASSP.1979.1163209
[7] Rabiner, L.R. and Schafer, R.W. (1978) Digital Processing of Speech Signals. Prentice Hall, Upper Saddle River.
[8] Quantieri, T.F. (2001) Discrete-Time Speech Signal Processing: Principles and Practice. Prentice Hall, Upper Saddle River.
[9] Allen, J. (1977) Short Term Spectral Analysis, Synthesis, and Modification by Discrete Fourier Transform. IEEE Transactions on Acoustic, Speech and Signal Processing, 25, 235-238.
http://dx.doi.org/10.1109/TASSP.1977.1162950

  
comments powered by Disqus

Copyright © 2020 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.