Skip to main content
SpringerLink
Log in

Speech enhancement by combining spectral subtraction and minimum mean square error-spectrum power estimator based on zero crossing

  • Published:
International Journal of Speech Technology Aims and scope Submit manuscript

Abstract

Speech data collected under uncontrolled environment need to be processed to build a robust automatic speech recognition system. In this paper, a method is proposed to process the degraded speech signal. Initially, the significance of the spectral subtraction with voice activity detection (SS-VAD) and magnitude squared spectrum estimators are studied for different types of noises. In SS-VAD method, the degraded speech data is sampled and windowed into 50% overlapping. The VAD is used to detect the voiced regions of speech signal. The minimum mean square error-short time power spectrum, minimum mean square error-spectrum power based on zero crossing (MMSE-SPZC) and maximum a posteriori estimators are studied individually. These MSS estimators are implemented on the assumption that the magnitude squared spectrum of the degraded speech signal is the sum of the clean (original) speech signal and noise model. The experimental results show that the MMSE-SPZC estimator gives better performance compared to the other two methods. This estimator is combined with SS-VAD method to improve the performance. In this paper, the combined SS-VAD and MMSE-SPZC method, yields better speech quality by reducing noise in degraded speech signal compared to the individual methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  • Beh, J., & Ko, H. (2003). A novel spectral subtraction scheme for robust speech recognition: Spectral subtraction using spectral harmonics of speech. In IEEE international conference on multimedia and expo, New York (Vol. 3, pp. I-648–I-651).

  • Berouti, M., Schwartz M., & Makhoul, J. (1979). Enhancement of speech corrupted by acoustic noise. In Proceedings of IEEE International conference on acoustics, speech and signal processing, Washington DC (pp. 208–211).

  • Boll, S. (1979). Suppression of acoustic noise in speech using spectral subtraction. IEEE Transactions on Acoustics Speech and Signal Processing, 27, 113–120.

    Article  Google Scholar 

  • Brungart, D. S., Chang, P. S., Simpson, B. D., & Wang, D. (2006). Isolating the energetic component of speech-on-speech masking with ideal time-frequency segregation. The Journal of the Acoustical Society of America, 120(6), 4007–4018.

    Article  Google Scholar 

  • Cohen, I., & Berdugo, B. (2002). Noise estimation by minima controlled recursive averaging for robust speech enhancement. IEEE Signal Processing Letters, 9(1), 12–15.

    Article  Google Scholar 

  • Cole, C., Karam, M., & Aglan, H. (2008). Spectral subtraction of noise in speech processing applications. In 40th Southeastern symposium system theory (pp. 50–53), SSST-2008, 16–18 March.

  • Computational Auditory Scene Analysis (CASA). (2006). In D. Wang & G. Brown (Eds.), Principles, algorithms, and applications. Piscataway, NJ: Wiley/IEEE Press.

  • Diethorn, E. J. (2004). Subband noise reduction methods for speech enhancement. In Y. Huang & J. Benesty (Eds.), Audio signal processing for next-generation multimedia communication systems (pp. 91–115). Boston: Springer.

    Chapter  Google Scholar 

  • Donoho, D. L., & Johnstone, I. M. (1994). Ideal spatial adaptation by wavelet shrinkage. Biometrika, 81(3), 425–455.

    Article  MathSciNet  MATH  Google Scholar 

  • Donoho, D. L., & Johnstone, I. M. (1995). Adapting to unknown smoothness via wavelet shrinkage. Journal of the American Statistical Association, 90(432), 1200–1224.

    Article  MathSciNet  MATH  Google Scholar 

  • Ephraim, Y., & Malah, D. (1984). Speech enhancement using a minimum mean square error short-time spectral amplitude estimator. IEEE Transactions on Acoustics Speech and Signal Processing, 32(6), 1109–1121.

    Article  Google Scholar 

  • Ephraim, Y., & Malah, D. (1985). Speech enhancement using a minimum mean square error log-spectral amplitude estimator. IEEE Transactions on Acoustics Speech and Signal processing, 33(2), 443–445.

    Article  Google Scholar 

  • Etter, W., & Moschytz, G. S. (1994). Noise reduction by noise-adaptive spectral magnitude expansion. Journal of the Audio Engineering Society, 42, 341–349.

    Google Scholar 

  • Evans, N. W. D., Mason, J. S., Liu, W. M., & Fauve, B. (2005). On the fundamental limitations of spectral subtraction: An assessment by automatic speech recognition. In Signal processing conference, 2005 13th European, Antalya (pp. 1-4).

  • Faller, C., & Chen, J. (2005). Suppressing acoustic echo in a spectral envelope space. IEEE Transactions on Speech and Audio Processing, 13(5), 1048–1062.

    Article  Google Scholar 

  • Gauvain, J. L., & Lee, C. H. (1994). Maximum a posteriori estimation for multivariate Gaussian mixture observations of Markov chains. IEEE Transactions on Speech and Audio Processing, 2(2), 291–299.

    Article  Google Scholar 

  • Goodarzi, H. M., & Seyedtabaii, S. (2009). Speech enhancement using spectral subtraction based on a modified noise minimum statistics estimation. In Fifth joint international conference (pp. 1339–1343), August 25–27, 2009.

  • Hu, Y., & Loizou, P. (2006). Subjective comparison of speech enhancement algorithms. In Proceedings of the IEEE international conference on acoustics, speech and signal processing, Toulouse (Vol. 1, pp. 153–156).

  • Hu, Y., & Loizou, P. (2007). Subjective comparison and evaluation of speech enhancement algorithms. Speech Communication, 49, 588–601.

    Article  Google Scholar 

  • Hu, Y. & Loizou, P. C. (2008). Evaluation of objective quality measures for speech enhancement. IEEE Transactions on Audio, Speech, and Language Processing, 16(1), 229–238.

    Article  Google Scholar 

  • ITU. (2000). Perceptual evaluation of speech quality (PESQ), and objective method for end-to-end speech quality assessment of narrowband telephone net- works and speech codecs. ITU, ITU-T Rec.

  • Jansen, M. (2001). Noise reduction by wavelet thresholding. Series lecture notes in statistics (Vol. 161). Berlin: Springer.

  • Kamath, S., & Loizou, P. (2002). A multi-band spectral subtraction method for enhancing speech corrupted by colored noise. In Proceedings of the IEEE international conference on acoustics, speech and signal processing, Orlando

  • Karam, M., Khazaal, H. F., Aglan, H., & Cole, C. (2014). Noise removal in speech processing using spectral subtraction. Journal of Signal and Information Processing, 5(2), 45989.

    Article  Google Scholar 

  • Kim, G., & Loizou, P. C. (2010). Improving speech intelligibility in noise using environment-optimized algorithms. IEEE Transactions on Audio Speech and Language Processing, 18(8), 2080–2090.

    Article  Google Scholar 

  • Kim, G., Lu, Y., Hu, Y., & Loizou, P. C. (2009). An algorithm that improves speech intelligibility in noise for normal-hearing listeners. The Journal of the Acoustical Society of America, 126(3), 1486–1494.

    Article  Google Scholar 

  • Li, N., & Loizou, P. (2008). Factors influencing intelligibility of ideal binary-masked speech: Implications for noise reduction. The Journal of the Acoustical Society of America, 123(3), 1673–1682.

    Article  Google Scholar 

  • Liu, H., Yu, X., Wan, W., & Swaminathan, R. (2012). An improved spectral subtraction method. In International conference on audio, language and image processing (ICALIP), Shanghai (pp. 790–793).

  • Loizou, P. C. (2005). Speech enhancement based on perceptually motivated bayesian estimators of the magnitude spectrum. IEEE Transactions on Speech and Audio Processing, 13(5), 857–869.

    Article  Google Scholar 

  • Loizou, P. (2007). Speech enhancement: Theory and practice (1st ed.). Boca Raton, FL: CRC Taylor & Francis.

    Book  Google Scholar 

  • Lotter, T., & Vary, P. (2005). Speech enhancement by map spectral amplitude estimation using a super-Gaussian speech model. EURASIP Journal on Advances in Signal Processing, 5(1), 1110–1126.

    MATH  Google Scholar 

  • Lu, Y., & Loizou, P. C. (2011). Estimators of the magnitude-squared spectrum and methods for incorporating SNR uncertainty. IEEE Transactions on Audio Speech and Language Processing, 19(5), 1123–1137.

    Article  Google Scholar 

  • Mallat, S. (1999). A wavelet tour of signal processing. San Diego, CA: Academic.

    MATH  Google Scholar 

  • Martin, R. (2001). Noise power spectral density estimation based on optimal smoothing and minimum statistics. IEEE Transactions on Speech and Audio Processing, 9(5), 504–512.

    Article  Google Scholar 

  • Martin, R. (2005). Speech enhancement based on minimum mean-square error estimation and supergaussian priors. IEEE Transactions on Speech and Audio Processing, 13(5), 845–856.

    Article  Google Scholar 

  • McAulay, R., & Malpass, M. (1980). Speech enhancement using a soft-decision noise suppression filter. IEEE Transactions on Acoustics Speech and Signal Processing, 28(2), 137–145.

    Article  Google Scholar 

  • Quackenbush, S., Barnwell, T., & Clements, M. (1988). Objective measures of speech quality. Englewood Cliffs, NJ: Prentice-Hall.

    Google Scholar 

  • Rabiner, L., & Juang, B. H. (1993). Fundamentals of speech recognition. Upper Saddle River, NJ: Prentice- Hall Inc.

    Google Scholar 

  • Ramirez, J., Gorriz, J. M., Segura, J. C., et al. (2003). Voice activity detection. Fundamentals and speech recognition system robustness. Rijeka: InTech.

    Google Scholar 

  • Rix, A., Beerends, J., Hollier, M., & Hekstra, A. (2001). Perceptual evaluation of speech quality (PESQ)—A new method for speech quality assessment of telephone networks and codecs. In Proceedings of the IEEE international conference on acoustics, speech and signal processing, Istanbul (Vol. 2, pp. 749–752).

  • Sim, B. L., Tong, Y. C., Chang, J. S., & Tan, C. T. (1998). A parametric formulation of the generalized spectral subtraction method. IEEE Transactions on Speech and Audio Processing, 6(4), 328–337.

    Article  Google Scholar 

  • Thimmaraja, Y. G., Jai Prakash, T. S., & Jayanna, H. S. (2015). Noise elimination in degraded Kannada speech signal for speech recognition. In IEEE proceedings of international conference on trends in automation, communication and computing technologies (ITACT-2015), Bangalore (pp. 183–186), December 21–22, 2015.

  • Wolfe, P. J., & Godsill, S. J. (2001). Simple alternatives to the Ephraim and Malah suppression rule for speech enhancement. In Proceedings of the 11th IEEE signal processing workshop on statistics and signal processing, Singapore (pp. 496–499).

  • Xia, B., Liang, Y., & Bao, C. (2009). A modified spectral subtraction method for speech enhancement based on masking property of human auditory system. In International conference on wireless communications signal processing, WCSP, Nanjing (pp. 1–5).

  • Yadava, T. G., & Jayanna, H. S. (2017). A spoken query system for the agricultural commodity prices and weather information access in Kannada language. International Journal of Speech Technology, Springer, 20(3), 635–644.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Siddaganga Institute of Technology, Tumkur, Karnataka, India

    Thimmaraja G. Yadava

  2. Department of Information Science and Engineering, Siddaganga Institute of Technology, Tumkur, Karnataka, India

    H. S. Jayanna

Authors
  1. Thimmaraja G. Yadava
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. H. S. Jayanna
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Thimmaraja G. Yadava.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yadava, T.G., Jayanna, H.S. Speech enhancement by combining spectral subtraction and minimum mean square error-spectrum power estimator based on zero crossing. Int J Speech Technol 22, 639–648 (2019). https://doi.org/10.1007/s10772-018-9506-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10772-018-9506-9

Keywords

Search

Navigation