Noise Estimation for Speech Enhancement Using Minimum-Spectral-Average and Vowel-Presence Detection Approach

  • Ching-Ta Lu
  • Yung-Yue Chen
  • Jun-Hong Shen
  • Ling-Ling Wang
  • Chung-Lin Lei
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 422)


The accuracy of noise estimation is important for the performance of a speech enhancement system. This study proposes using variable segment length for noise tracking and variable thresholds for the determination of speech-presence probability. Initially, the fundamental frequency is estimated to determine whether a frame is a vowel. In the case of a vowel frame, the segment length increases; meanwhile the threshold for speech-presence is decreased. So the noise magnitude is adequately underestimated. The speech distortion is accordingly reduced in enhanced speech. Conversely, the segment length is rapidly decreased during noise-dominant regions. This enables the noise estimate to be updated quickly and the noise variation to be well tracked, yielding background noise being efficiently removed by the process of speech enhancement. Experimental results show that the proposed method can efficiently track the variation of background noise, enabling the performance of speech enhancement to be improved.


Noise estimation Variable segment length Speech enhancement Harmonic adaptation Minimum-Spectral-Average 



This research was supported by the Ministry of Science and Technology, Taiwan, under contract numbers MOST 104-2221-E-468-007, and MOST 104-2628-E-006-012-MY3.


  1. 1.
    Kianyfar, A., Abutalebi, H. R.: Improved Speech Enhancement Method Based on Auditory Filterbank and Fast Noise Estimation. In: International Symposium on Telecommunications, pp. 441–445 (2014)Google Scholar
  2. 2.
    Krawczyk-Becker, M., Fischer, D., Gerkmann, T.: Utilizing Spectro-Temporal Correlations for an Improved Speech Presence Probability Based Noise Power Estimation. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 365–369 (2015)Google Scholar
  3. 3.
    Cohen, I. Berdugo, B.: Noise Estimation By Minima Controlled Recursive Averaging for Robust Speech Enhancement. IEEE Signal Process. Lett., vol. 9, no. 1, pp. 12–15 (2002)Google Scholar
  4. 4.
    Cohen, I.: Noise Spectrum Estimation in Adverse Environments: Improved Minima Controlled Recursive Averaging. IEEE Trans. Speech Audio Process., vol. 11, no. 5, pp. 466–475 (2003)Google Scholar
  5. 5.
    Fan, N., Rosca, J., Balan, R.: Speech Noise Estimation Using Enhanced Minima Controlled Recursive Averaging. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 4, pp. 581–584 (2007)Google Scholar
  6. 6.
    Kum, J. M., Chang, J. H.: Speech Enhancement Based on Minima Controlled Recursive Averaging Incorporating Second-Order Conditional Map Criterion. IEEE Signal Process. Lett., vol. 16, no. 7, pp. 624–627 (2009)Google Scholar
  7. 7.
    Wu, D., Zhu, W. P., Swamy, M. N. S.: Noise Spectrum Estimation with Improved Minimum Controlled Recursive Averaging Based on Speech Enhancement Residue. In: IEEE International Midwest Symposium on Circuits and Systems, pp. 948–951 (2012)Google Scholar
  8. 8.
    Chen, Y. J., Wu, J. L.: Forward-Backward Minima Controlled Recursive Averaging to Speech Enhancement. In IEEE International Symposium on Computational Intelligence for Multimedia, Signal Vision Processing, pp. 49–52 (2013)Google Scholar
  9. 9.
    Yong, P. C., Nordoholm, S., Dam, H. H.: Noise Estimation with Low Complexity for Speech Enhancement. In IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, pp. 109–112(2011)Google Scholar
  10. 10.
    Mai, V. K., Pastor, D., Aissa-EI-Bey, A., Le-Bidan, R.: Robust Estimation of Non-stationary Noise Power Spectrum for Speech Enhancement. IEEE/ACM Trans. Audio, Speech, Lang. Process., vol. 23, no. 4, pp. 670–682 (2015)Google Scholar
  11. 11.
    Lu, C. –T., Shen, J. –H., Tseng, K. –F..: Speech Enhancement Using Three-Step- Decision Gain Factor with Optimal Smoothing. Int. J. Electr. Eng., vol. 18, no. 5, pp. 209–221 (2011)Google Scholar
  12. 12.
    Virag, N.: Single Channel Speech Enhancement Based on Masking Properties of the Human Auditory System. IEEE Trans Speech Audio Process., vol. 7, no. 2, pp. 126–137 (1999)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Ching-Ta Lu
    • 1
  • Yung-Yue Chen
    • 2
  • Jun-Hong Shen
    • 1
  • Ling-Ling Wang
    • 1
  • Chung-Lin Lei
    • 1
  1. 1.Department of Information CommunicationAsia UniversityTaichungTaiwan, ROC
  2. 2.Department of Systems and Naval Mechatronics EngineeringNational Cheng Kung UniversityTainanTaiwan, ROC

Personalised recommendations