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Bone Conducted Speech Signal Enhancement Using LPC and MFCC

  • Premjeet SinghEmail author
  • Manoj Kumar MukulEmail author
  • Rajkishore PrasadEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11278)

Abstract

The air microphone used in communication devices to acquire speech signal gathers highly imperceptible signal in noisy background conditions. Bone conducted speech signal appears to be a promising tool to avoid this situation and improve the quality of communication between two users because of its inherent capability of attenuating high frequency signals. Though, there is no background noise present, the quality of extracted bone conducted signal is usually quite low in terms of intelligibility and strength. The reason for this quality degradation can again be accounted to the high frequency signal repulsion nature of bones. To rectify this issue and to make the bone conducted signal useful in communication systems, some signal processing schemes are required to be developed. This paper introduces application of two signal processing schemes which are very commonly used in speech recognition systems, Linear Predictive Coding (LPC) and MFCC (Mel Frequency Cepstral Coefficient), to enhance the bone conducted signal and shows comparison between them. Results of the analysis show that slight improvement in noise reduction is possible by using the proposed techniques. However, retrieval of lost information, due to bone conduction of speech, cannot be achieved by any of the two proposed techniques and a more robust scheme has to be developed for bone conducted signal improvement.

Keywords

Bone conducted signal Air microphone signal LPC MFCC 

References

  1. 1.
    Hadei, S.A., et al.: A family of adaptive filter algorithms in noise cancellation for speech enhancement. Int. J. Comput. Electr. Eng. 2(2), 307 (2010)CrossRefGoogle Scholar
  2. 2.
    McBride, M., Tran, P., Letowski, T., Patrick, R.: The effect of bone conduction microphone locations on speech intelligibility and sound quality. Appl. Ergon. 42(3), 495–502 (2011)CrossRefGoogle Scholar
  3. 3.
    Munger, J.B., Thomson, S.L.: Frequency response of the skin on the head and neck during production of selected speech sounds. J. Acoust. Soc. Am. 124(6), 4001–4012 (2008)CrossRefGoogle Scholar
  4. 4.
    Prasad, R., Koike, T., Matsuno, F.: Speech signal captured by PVDF sensor. In: SICE Annual Conference, pp. 9–12. IEEE (2008)Google Scholar
  5. 5.
    Rahman, M.S., Shimamura, T.: Intelligibility enhancement of bone conducted speech by an analysis-synthesis method. In: 2011 IEEE 54th International Midwest Symposium on Circuits and Systems (MWSCAS), pp. 1–4. IEEE (2011)Google Scholar
  6. 6.
    Rahman, M.S., Shimamura, T.: A study on amplitude variation of bone conducted speech compared to air conducted speech. In: 2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), pp. 1–5. IEEE (2013)Google Scholar
  7. 7.
    Shimamura, T., Mamiya, J., Tamiya, T.: Improving bone-conducted speech quality via neural network. In: 2006 IEEE International Symposium on Signal Processing and Information Technology, pp. 628–632. IEEE (2006)Google Scholar
  8. 8.
    Shimamura, T., Tamiya, T.: A reconstruction filter for bone-conducted speech. In: 48th Midwest Symposium on Circuits and Systems, pp. 1847–1850. IEEE (2005)Google Scholar
  9. 9.
    Tychtl, Z., Psutka, J.: Speech production based on the mel-frequency cepstral coefficients. In: Sixth European Conference on Speech Communication and Technology (1999)Google Scholar
  10. 10.
    tat Vu, T., Unoki, M., Akagi, M.: A study on an LP-based model for restoring bone-conducted speech. In: First International Conference on Communications and Electronics, ICCE 2006, pp. 294–299. IEEE (2006)Google Scholar
  11. 11.
    tat Vu, T., Unoki, M., Akagi, M.: An LP-based blind model for restoring bone-conducted speech. In: Second International Conference on Communications and Electronics, ICCE 2008, pp. 212–217. IEEE (2008)Google Scholar
  12. 12.
    Won, S.Y., Berger, J.: Estimating transfer function from air to bone conduction using singing voice. In: ICMC (2005)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Birla Institute of Technology, MesraRanchiIndia
  2. 2.B.N. CollegePatnaIndia

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