Skip to main content
SpringerLink
Log in

Generalized Subspace Snoring Signal Enhancement Based on Noise Covariance Matrix Estimation

  • Published:
Circuits, Systems, and Signal Processing Aims and scope Submit manuscript

Abstract

Acoustical properties of snoring signal have been widely studied as a potentially cost-effective and reliable alternative to diagnosing obstructive sleep apnea hypopnea syndrome, with a common recognition that the diagnostic accuracy depends heavily on the snoring signal quality. In the paper, generalized subspace noise reduction based on noise covariance matrix estimate is proposed. The noise covariance matrix is the Toeplitz matrix of the unbiased autocorrelation sequence which is estimated by recursive averaging its past value adjusted by a time-varying smoothing parameter controlled by the snoring signal presence probability, and the signal presence is determined by the ratio of temporal frame autocorrelation value to its minimum absolute value. The proposed method has a better estimate of noise covariance matrix, and the results of objective quality measurements and spectrograms of snoring signal show obvious improvement in terms of noise reduction and signal distortion under different non-stationary noise environments compared with conventional subspace enhancement algorithm.

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
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Data Availability Statement

The data used to support the findings of this study are available from the corresponding author upon request.

References

  1. A. Azarbarzin, Z. Moussavi, Snoring sounds variability as a signature of obstructive sleep apnea. Med. Eng. Phys. 35(4), 479–485 (2013)

    Article  Google Scholar 

  2. S. Boll, Suppression of acoustic noise in speech using spectral subtraction. IEEE Trans. Acoust. Speech Signal Process. 27(2), 113–120 (1979)

    Article  Google Scholar 

  3. I. Cohen, B. Berdugo, Noise estimation by minima controlled recursive averaging for robust speech enhancement. IEEE Signal Process. Lett. 9(1), 12–15 (2002)

    Article  Google Scholar 

  4. E. Dafna, A. Tarasiuk, Y. Zigel, Automatic detection of whole night snoring events using non-contact microphone. PLoS ONE 8(12), e84139 (2013)

    Article  Google Scholar 

  5. R. Dahlan, AdaBoost noise estimator for subspace based speech enhancement, in 2018 International Conference on Computer, Control, Informatics and its Applications: Recent Challenges in Machine Learning for Computing Applications, IC3INA 2018—Proceeding (2019), pp. 110–113

  6. M. Dendrinos, S. Bakamidis, G. Carayannis, Speech enhancement from noise: a regenerative approach. Speech Commun. 10(1), 45–57 (1991)

    Article  Google Scholar 

  7. Y. Ephraim, D. Malah, Speech enhancement using a minimum mean-square error log-spectral amplitude estimator. IEEE Trans. Acoust. Speech Signal Process. 33(2), 443–445 (1985)

    Article  Google Scholar 

  8. Y. Ephraim, H.L. Van Trees, A signal subspace approach for speech enhancement. IEEE Trans. Speech Audio Process. 3(4), 251–266 (1995)

    Article  Google Scholar 

  9. G. Farahani, S.M. Ahadi, M.M. Homayounr, A. Kashi, Robust feature extraction using spectral peaks of the filtered higher lag autocorrelation sequence of the speech signal, in 2006 IEEE International Symposium on Signal Processing and Information Technology (IEEE, New York, 2006), pp. 896–901

  10. G. Farahani, Autocorrelation-based noise subtraction method with smoothing, overestimation, energy, and cepstral mean and variance normalization for noisy speech recognition. Eurasip J. Audio Speech Music Process. 1, 2017 (2017)

    Google Scholar 

  11. N. Faraji, S.M. Ahadi, Improved subspace-based speech enhancement using a novel updating approach for noise correlation matrix, in 2015 Signal Processing and Intelligent Systems Conference (SPIS) (IEEE, New York, 2015), pp. 88–92

  12. E. Grivel, M. Gabrea, M. Najim, Speech enhancement as a realisation issue. Signal Process. 82(12), 1963–1978 (2002)

    Article  Google Scholar 

  13. Y. Hu, P.C. Loizou, A generalized subspace approach for enhancing speech corrupted by colored noise. IEEE Trans. Speech Audio Process. 11(44), 334–341 (2003)

    Article  Google Scholar 

  14. F. Jabloun, B. Champagne, Incorporating the human hearing properties in the signal subspace approach for speech enhancement. IEEE Trans. Speech Audio Process. 11(6), 700–708 (2003)

    Article  Google Scholar 

  15. S.H. Jensen, P.C. Hansen, S.D. Hansen, J.A. Sorensen, Reduction of broad-band noise in speech by truncated QSVD. IEEE Trans. Speech Audio Process. 3(6), 439–448 (1995)

    Article  Google Scholar 

  16. T. Jiang, R. Liang, Q. Wang, Y. Bao, Speech noise reduction algorithm in digital hearing aids based on an improved sub-band SNR estimation. Circuits Syst. Signal Process. 37(3), 1243–1267 (2018)

    Article  MathSciNet  Google Scholar 

  17. Y. Jiang, J. Peng, X. Zhang, Automatic snoring sounds detection from sleep sounds based on deep learning. Phys. Eng. Sci. Med. 43(2), 679–689 (2020)

    Article  Google Scholar 

  18. A.S. Karunajeewa, U.R. Abeyratne, C. Hukins, Silence-breathing-snore classification from snore-related sounds. Physiol. Meas. 29(2), 227–243 (2008)

    Article  Google Scholar 

  19. P.C. Loizou, Speech enhancement: theory and practice (CRC Press, Boca Raton, 2013)

    Book  Google Scholar 

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

    Article  Google Scholar 

  21. R. Martin, D. Malah, R.V. Cox, A.J. Accardi, A noise reduction preprocessor for mobile voice communication. EURASIP J. Adv. Signal Process. 2004(8), 147306 (2004)

    Article  Google Scholar 

  22. L.T. McWhorter, L.L. Scharf, Multiwindow estimators of correlation. IEEE Trans. Signal Process. 46(2), 440–448 (1998)

    Article  Google Scholar 

  23. S. Miyazaki, Y. Itasaka, K. Ishikawa, K. Togawa, Acoustic analysis of snoring and the site of airway obstruction in sleep related respiratory disorders. Acta Otolaryngol. Suppl. 118(537), 47–51 (1998)

    Article  Google Scholar 

  24. S.M. Mousavi, N. Alikar, S.T. Akhavan Niaki, Seyed Mohsen Mousavi, An improved fruit fly optimization algorithm to solve the homogeneous fuzzy series-parallel redundancy allocation problem under discount strategies. Soft. Comput. 20(6), 2281–2307 (2016)

    Article  Google Scholar 

  25. A.K. Ng, T.S. Koh, K. Puvanendran, U.R. Abeyratne, Snore signal enhancement and activity detection via translation-invariant wavelet transform. IEEE Trans. Biomed. Eng. 55(10), 2332–2342 (2008)

    Article  Google Scholar 

  26. P.E.N.G. Jianxin, T.A.N.G. Yunfei, Noise reduction of snoring sound by using traditional spectral subtraction and wiener filter. J. South China Univ. Technol. (Nat. Sci. Ed.) 46(3), 16 (2018)

    Google Scholar 

  27. W.J. Riley, Hamilton Technical Services. Use of the Autocorrelation Function for Frequency Stability Analysis @BULLET Examples of Autocorrelation Plots for Power Law Noise (2003)

  28. A.W. Rix, J.G. Beerends, M.P. Hollier, A.P Hekstra, Perceptual evaluation of speech quality (PESQ)-a new method for speech quality assessment of telephone networks and codecs, in 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No. 01CH37221), vol 2 (IEEE, New York, 2001), pp. 749–752

  29. A. Saadoune, A. Amrouche, S.A. Selouani, MCRA noise estimation for KLT-VRE-based speech enhancement. Int. J. Speech Technol. 16(3), 333–339 (2013)

    Article  Google Scholar 

  30. E. Sejdić, I. Djurović, J. Jiang, Time-frequency feature representation using energy concentration: an overview of recent advances. Digital Signal Process. Rev. J. 19(1), 153–183 (2009)

    Article  Google Scholar 

  31. F.K. Shiomi, I.T. Pisa, C.J.R. de Campos, Computerized analysis of snoring in sleep Apnea Syndrome. Braz. J. Otorhinolaryngol. 77(4), 488–498 (2011)

    Article  Google Scholar 

  32. J. Sun, J. Zhang, M. Small, Extension of the local subspace method to enhancement of speech with colored noise. Signal Process. 88(7), 1881–1888 (2008)

    Article  Google Scholar 

  33. S. Surendran, T.K. Kumar, Variance normalized perceptual subspace speech enhancement. AEU Int. J. Electron. Commun. 74, 44–54 (2017)

    Article  Google Scholar 

  34. M. Thiagarajan, J. Natarajan, K.M. Sharavanaraju, Pitch-based voice activity detection for feedback cancellation and noise reduction in hearing aids. Circuits Syst. Signal Process. 37(10), 4504–4526 (2018)

    Article  Google Scholar 

  35. A. Varga, H.J.M. Steeneken, Assessment for automatic speech recognition: II. NOISEX-92: a database and an experiment to study the effect of additive noise on speech recognition systems. Speech Commun. 12, 247–251 (1993)

    Article  Google Scholar 

  36. X. Xie, D. Yue, J.H. Park, Observer-based fault estimation for discrete-time nonlinear systems and its application: a weighted switching approach. IEEE Trans. Circuits Syst. I Regul. Pap. 66(11), 4377–4387 (2019)

    Article  MathSciNet  Google Scholar 

  37. H. Xu, W. Huang, L. Yu, L. Chen, Sound spectral analysis of snoring sound and site of obstruction in obstructive sleep apnea syndrome. Acta Otolaryngol. 130(10), 1175–1179 (2010)

    Article  Google Scholar 

  38. C.H. You, S.N. Koh, S. Rahardja, An invertible frequency eigendomain transformation for masking-based subspace speech enhancement. IEEE Signal Process. Lett. 12(6), 461–464 (2005)

    Article  Google Scholar 

  39. C.H. You, S.N. Koh, S. Rahardja, Signal subspace speech enhancement for audible noise reduction, in Proceedings IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005 (ICASSP’05), vol 1 (IEEE, New York, 2005), pp. 1–145

Download references

Acknowledgements

This work was supported by National Natural Science Foundation of China under Grant Nos. 11974121, 81570904 and National Youth Foundation of China under Grant No. 81900927.

Author information

Authors and Affiliations

  1. School of Physics and Optoelectronics, South China University of Technology, Guangzhou, 510640, China

    Li Ding, Jianxin Peng & Yanmei Jiang

  2. State Key Laboratory of Respiratory Disease, Department of Otolaryngology-Head and Neck Surgery, Laboratory of ENT-HNS Disease, First Affiliated Hospital, Guangzhou Medical University, Guangzhou, 510120, China

    Lijuan Song

Authors
  1. Li Ding
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jianxin Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yanmei Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lijuan Song
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jianxin Peng.

Ethics declarations

Conflicts of interest

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Ethical Approval

This study was approved by the Ethics Committee of Guangzhou Medical University, and an informed consent was obtained from each participant.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ding, L., Peng, J., Jiang, Y. et al. Generalized Subspace Snoring Signal Enhancement Based on Noise Covariance Matrix Estimation. Circuits Syst Signal Process 40, 3355–3373 (2021). https://doi.org/10.1007/s00034-020-01623-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00034-020-01623-3

Keywords

Search

Navigation