Abstract
Spectral subtraction is a method for restoration of the power or the magnitude spectrum of a signal observed in additive noise, through subtraction of an estimate of the average noise spectrum from the noisy signal spectrum. The noise spectrum is estimated, and updated, from the periods when the signal is absent and only the noise is present. The assumption is that the noise is a stationary or a slowly varying process, and that the noise spectrum does not change significantly in-between the update periods. For restoration of time-domain signals, an estimate of the instantaneous magnitude spectrum is combined with the phase of the noisy signal, and then transformed via an inverse discrete Fourier transform to the time domain. In terms of computational complexity spectral subtraction is relatively inexpensive. However, due to random variations of noise, spectral subtraction can result in negative estimates of the short-time magnitude or power spectrum. The magnitude and power spectrum are non-negative variables, and any negative estimate of these variables should be mapped into a non-negative value. This nonlinear rectification process distorts the distribution of the restored signal. The processing distortion becomes more noticeable as the signal to noise ratio decreases. In this chapter we study spectral subtraction, and the different methods of reducing and removing the processing distortions.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Bibliography
Boll S.F (1979), Suppression of Acoustic Noise in Speech Using Spectral Subtraction IEEE Transactions, Vol. ASSP-27, No 2, Pages 113–20, April.
Brouti M., Schwartz R., Makhoul J. (1979), Enhancement of Speech Corrupted by Acoustic Noise, Proc. IEEE, Int. Conf. on Acoustics, Speech and Signal Processing, ICASSP-79, Pages 208–11.
Crozier P.M. et al (1993), The Use of Linear Prediction and Spectral Scaling For Improving Speech Enhancement, EuroSpeech-93, Pages 231–2343.
Ephraim Y., (1992), Statistical Model Based Speech Enhancement systems, Proc. IEEE, Vol. 80, No. 10, Pages 1526–55.
Ephraim Y. and Van Trees H.L. (1993), A Signal Subspace Approach for Speech Enhancement, ICASSP-93, Pages 355–58.
Juang B. H., Rabiner L. R. (1987), Signal Restoration by Spectral Mapping, Proc. IEEE, Int. Conf. on Acoustics, Speech and Signal Processing, ICASSP-87 Texas.
Kobayashi T. et al (1993), Speech recognition under the nonstationary noise based on the noise hidden Markov model and spectral subtraction, EUROSPEECH-93, Pages 833–837.
Lim J. S. (1978), Evaluations of Correlation Subtraction Method for Enhancing Speech Degraded by Additive White Noise, IEEE Trans. Acoustics, Speech and Signal Processing, Vol. ASSP-26, No. 5, Pages 471–472.
Lockwood P., Boudy J. (1992), Experiments with a Nonlinear Spectral Subtractor (NSS), Hidden Markov Models and the Projection, for Robust Speech Recognition in Car, Speech Communications, Elsevier, Pages 215–228.
Lockwood P. et al (1992), Non-Linear Spectral Subtraction and Hidden Markov Models for Robust Speech Recognition to Car Noise Environments, ICASSP-92, Pages 265–268.
Milner B. P. (1995), Speech Recognition in Adverse Environments, PhD Thesis, University of East Anglia, UK.
McAulay R.J., Malpass M.L. (1980), Speech enhancement using a soft-decision noise suppresion filter, IEEE Trans. ASSP, Vol. 28, no. 2, Pages 137–145, April.
Nolazco-Flores JA, Young SJ. (1994),Adapting a HMM-based Recogniser for Noisy Speech Enhanced by Spectral Subtraction, Proc. IEEE, Int. Conf. on Acoustics, Speech and Signal Processing, ICASSP-94 Adelaide.
Porter J.E., Boll S.F. (1984), Optimal Estimators for Spectral Restoration of Noisy Speech, ICASSP-84, Pages 18A.2.1–18A.2.4.
O’Shaughnessy D. (1989), Enhancing Speech Degraded by Additive Noise or Interfering Speakers, IEEE Commun. Mag. Pages 46–52.
Pollak P. et al (1993), Noise suppression system for a car, EuroSpeech-93, Pages 1073–1076.
Sorenson H.B (1993), Robust Speaker Independent Speech Recognition Using Non-Linear Spectral Subtraction Based IMELDA, EuroSpeech-93, Pages 235–238.
Sondhi M. M., Schmidt C. E., Rabiner R. (1981), Improving the Quality of a Noisy Speech Signal, Bell Syst. Tech. J., Vol. 60, No. 8, Pages 1847–1859.
Van Compernolle D. (1989), Noise Adaptation in a Hidden Markov Model Speech Recognition System, Computer Speech and Language, Vol. 3, Pages 151–167.
Vaseghi S.V., Frayling-Corck R. (1993), Restoration of Archived Gramophone Records, Journal of Audio Engineering Society.
Xie F.(1993), Speech Enhancement by Non-Linear Spectral Estimation a Unifying Approach, EuroSpeech-93, Pages 617–620.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 1996 John Wiley & Sons Ltd. and B.G. Teubner
About this chapter
Cite this chapter
Vaseghi, S.V. (1996). Spectral Subtraction. In: Advanced Signal Processing and Digital Noise Reduction. Vieweg+Teubner Verlag. https://doi.org/10.1007/978-3-322-92773-6_9
Download citation
DOI: https://doi.org/10.1007/978-3-322-92773-6_9
Publisher Name: Vieweg+Teubner Verlag
Print ISBN: 978-3-322-92774-3
Online ISBN: 978-3-322-92773-6
eBook Packages: Springer Book Archive