Skip to main content
SpringerLink
Log in

Speech Enhancement Based on Analysis–Synthesis Framework with Improved Parameter Domain Enhancement

  • Published:
Journal of Signal Processing Systems Aims and scope Submit manuscript

Abstract

This paper presents a speech enhancement approach based on analysis–synthesis framework. An improved multi-band summary correlogram (MBSC) algorithm is proposed for pitch estimation and voiced/unvoiced (V/UV) detection. The proposed pitch detection algorithm achieves a lower pitch detection error compared with the reference algorithm. The denoising autoencoder (DAE) is applied to enhance the line spectrum frequencies (LSFs). The reconstruction loss could be decreased compare with the swallow model. The proposed approach is evaluated using the perceptual evaluation of speech quality (PESQ) and the experimental results show that the proposed approach improves the performance of speech enhancement compared with the conventional speech enhancement approach. In addition, it could be applied to parametric speech coding even at low bit rate and low signal-noise ratio (SNR) environments.

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

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5

Similar content being viewed by others

References

  1. Boll, S. (1979). Suppression of acoustic noise in speech using spectral subtraction. IEEE Transactions on Acoustics, Speech and Signal Processing, 27(2), 113–120.

    Article  Google Scholar 

  2. 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 

  3. Paliwal, K., Schwerin, B., & Wójcicki, K. (2012). Speech enhancement using a minimum mean-square error short-time spectral modulation magnitude estimator. Speech Communication, 54(2), 282–305.

    Article  Google Scholar 

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

    Article  Google Scholar 

  5. Cohen, I. (2002). Optimal speech enhancement under signal presence uncertainty using log-spectral amplitude estimator. IEEE Signal Processing Letters, 9(4), 113–116.

    Article  Google Scholar 

  6. Sreenivas, T. V., & Kirnapure, P. (1996). Codebook constrained Wiener filtering for speech enhancement. IEEE Transactions on Speech and Audio Processing, 4(5), 383–389.

    Article  Google Scholar 

  7. Mohammadiha, N., Martin, R., & Leijon, A. (2013). Spectral domain speech enhancement using HMM state-dependent super-Gaussian priors. IEEE Signal Processing Letters, 20(3), 253–256.

    Article  Google Scholar 

  8. Xie, F., & Compernolle, D. V. (1994). A family of MLP based nonlinear spectral estimators for noise reduction. In Acoustics, Speech, and Signal Processing (ICASSP) (pp. 53–56). Australia.

  9. Dahl, G. E., Sainath, T. N., Hinton, G. E. (2013). Improving deep neural networks for LVCSR using rectified linear units and dropout. In Acoustics, Speech, and Signal Processing (ICASSP) (pp. 8609–8613). Canada.

  10. Chen, R. F., Chan, C. F., So H. C. (2010). Noise suppression based on an analysis–synthesis approach. In Proc. Eur. Signal Process. Conf. (EUSIPCO) (pp. 1539–1543).

  11. Cheveigne, A., & Kawahara, H. (2002). YIN, a fundamental frequency estimator for speech and music. Journal of the Acoustical Society of America, 111, 1917–1930.

    Article  Google Scholar 

  12. Camacho, A., & Harris, J. (2008). A sawtooth waveform inspired pitch estimator for speech and music. Journal of the Acoustical Society of America, 124, 1638–1652.

    Article  Google Scholar 

  13. Rouat, J., Liu, Y., & Morissette, D. (1997). A pitch determination and voiced/unvoiced decision algorithm for noisy speech. Speech Communication, 21, 191–207.

    Article  Google Scholar 

  14. Tan, L. N., Alwan, A. (2011). Noise-robust F0 estimation using SNRweighted summary correlograms from multi-band comb filters. In Proc. IEEE ICASSP (pp. 4464–4467).

  15. Chen, R. F., Chan, C. F., & So, H. C. (2012). Model-based speech enhancement with improved spectral envelope estimation via dynamics tracking. IEEE Transactions on Speech and Audio Processing, 20(4), 1324–1336.

    Article  Google Scholar 

  16. Toda T., Saruwatari, H., Shikano, K. (2001). Voice conversion algorithm based on gaussian mixture model with dynamic frequency warping of straight spectrum. In Proc of ICASSP (pp. 941–944).

  17. Park, K. Y., & Kim, H. S. (2000). Narrowband to wideband conversion of speech using GMM based transformation. Proceeding of IEEE International Conference on Acoustics, Speech, Signal Processing, 4, 1843–1846.

    Google Scholar 

  18. Hinton, G. E., Osindero, S., & Teh, Y. W. (2006). A fast learning algorithm for deep belief nets. Neural Computation, 18(7), 1527–1554.

    Article  MATH  MathSciNet  Google Scholar 

  19. Bengio, Y., Yao, L., Alain, G., et al. (2013). Generalized denoising autoencoders as generative models. In Advances in Neural Information Processing Systems (pp: 899–907). USA.

  20. Tan, L. N., Alwan, A. (2013). Multi-band summary correlogram-based pitch detection for noisy speech. In Speech communication (pp. 841–856).

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

    Article  Google Scholar 

  22. Supplee, L. M., Cohn, R. P., Collura, J. S., McCree, A. V. (1997). MELP: the new federal standard at 2400bps. In Acoustics Speech and Signal Processing (1591–1594). Germany.

  23. Garofolo, J. S. (1993). TIMIT: Acoustic-phonetic Continuous Speech Corpus, Linguistic Data Consortium.

  24. Rice University, NOISEX-92 Database, [Online] Available: http://spib.rice.edu/spib/select noise.html.

  25. Chu, W., Alwan, A. (2009). reducing F0 frame error of F0 tracking algorithm under noisy condition with an unvoiced/voiced classification frontend. In Acoustics Speech and Signal Processing (3969-3972). Germany

  26. Jabloun, F., & Champagne, B. (2003). Incorporating the human hearing properties in the signal subspace approach for speech enhancement. IEEE Transactions on Speech and Audio Processing, 11(6), 700–708.

    Article  Google Scholar 

  27. Rix, A. W., Beerends, J. G., Hollier, M. P., et al. (2001). Perceptual evaluation of speech quality (PESQ)-a new method for speech quality assessment of telephone networks and codecs. In Acoustics, Speech, and Signal Processing (ICASSP) (pp. 749–752). USA.

  28. Talkin, D. (1995). Speech Coding and Synthesis. Elsevier (pp. 497–518).

  29. Kotnik, B., Hoge, H., Kacic, Z. (2006). Evaluation of Pitch Detection Algorithms in Adverse Conditions. Proc. 3rd International Conference on Speech Prosody (pp. 149–152). Dresden, Germany.

Download references

Acknowledgments

This work is supported by the National High-Tech Research and Development Program of China(863 Program) (No.2015AA016305), the National Natural Science Foundation of China (NSFC) (No.61425017, No.61403386, No.61305003, No.61332017, No.61375027, No.61273288, No.61233009, No.61203258), the Major Program for the National Social Science Fund of China (13&ZD189) and the Integration and application of basic science data in Chinese information processing field (XXH12504-1-11).

Author information

Authors and Affiliations

  1. National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

    Bin Liu, Jianhua Tao & Zhengqi Wen

  2. Institute of Acoustics, Chinese Academy of Sciences, Beijing, 100190, China

    Fuyuan Mo

Authors
  1. Bin Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jianhua Tao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhengqi Wen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Fuyuan Mo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bin Liu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, B., Tao, J., Wen, Z. et al. Speech Enhancement Based on Analysis–Synthesis Framework with Improved Parameter Domain Enhancement. J Sign Process Syst 82, 141–150 (2016). https://doi.org/10.1007/s11265-015-1025-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11265-015-1025-1

Keywords

Search

Navigation