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Fundamental Frequency Extraction of Noisy Speech Using Exponent Enhancement in Weighted Autocorrelation

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Sentimental Analysis and Deep Learning

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1408))

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Abstract

This research proposes a powerful method for extracting fundamental frequencies from speech in noisy environments that is more successful for speech processing applications. This work discusses a noise-resistant method for fundamental frequency extraction based on an exponent augmentation in the weighted autocorrelation function. To demonstrate the greater accuracy for fundamental frequency extraction, we focus on the exponent of the magnitude difference function. According to experimental results, proposed approach’s presentation in noisy situations provides an unequaled presentation compared to the conventional technique when an appropriate exponent is used.

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References

  1. Hess, W. (1983). Pitch determination of speech signals. Springer.

    Google Scholar 

  2. Rabiner, L. R., Cheng, M. J., Rosenberg, A. E., & McGonegal, C. A. (1976). A comparative performance study of several pitch detection algorithms. IEEE Transactions on Acoustics, Speech, and Signal Processing, 24(5), 339–417.

    Article  Google Scholar 

  3. Rabiner, L. R. (1977). On the use of autocorrelation analysis for pitch detection. IEEE Transactions on Acoustics, Speech, and Signal Processing, 25(1), 24–33.

    Article  Google Scholar 

  4. Ross, M. J., et al. (1974). Average magnitude difference function pitch extractor. IEEE Transactions on Acoustics, Speech, and Signal Processing, 22(5), 353–362.

    Article  Google Scholar 

  5. Xu, J. W., & Principle, J. C. (2008). A pitch detector based on a generalized correlation function. IEEE Transactions on Audio, Speech, and Language Processing, 16(8), 1420–1432.

    Article  Google Scholar 

  6. Shimamura, T., & Kobayashi, H. (2001). Weighted autocorrelation for pitch extraction of noisy speech. IEEE Transactions on Speech and Audio Processing, 9(7), 727–730.

    Article  Google Scholar 

  7. Noll, A. M. (1967). Cepstrum pitch determination. The Journal of the Acoustical Society of America, 41(2), 293–309.

    Article  Google Scholar 

  8. Kobayashi, H., & Shimamura, T. (1998). A modified cepstrum method for pitch extraction. In Proceedings of IEEE Asia-Pacifc International Conference on Circuits and Systems Microelectronics and Integrating Systems (APCCAS).

    Google Scholar 

  9. Gonzalez, S., & Brookes, M. (2014). PEFAC–A pitch estimation algorithm robust to high levels of noise. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 22(2), 518–530.

    Article  Google Scholar 

  10. Yang, N., Ba, H., Cai, W., Demirkol, I., & Heinzelman, W. (2014). BaNa: A noise resilient fundamental frequency detection algorithm for speech and music. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 22(12), 1833–1848.

    Article  Google Scholar 

  11. Motegi, S., & Shimamura, T. (2012). Extended fundamental frequency extraction using exponentiated amplitude spectrum with band-limitation. International Journal of Computer and Electrical Engineering, 4(4), 507–510.

    Article  Google Scholar 

  12. Narita, M., & Shimamura, T. (2011) Exponentiated enhancement for fundamental frequency extraction of noisy speech. IEEE International Symposium on Signal Process. and Information Technology (pp. 342–346).

    Google Scholar 

  13. 20 Countries Language Database. (1988). NTT Advanced Technology Corporation, Japan.

    Google Scholar 

  14. Plante, F., Meyer, G., & Ainsworth, W. (1995). A fundamental frequency extraction reference database. In Proceedings of Eurospeech (pp. 837–840).

    Google Scholar 

  15. Brookes, M. Voicebox toolkit [Online]. Available, www.ee.ic.ac.uk/hp/staff/dmb/voicebox/voicebox.html.

  16. Wcng, wireless communication networking group, [Online]. Available www.ece.rochester.edu/projects/wcng/code.html.

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Author information

Authors and Affiliations

  1. Comilla University, Cumilla, Bangladesh

    Md. Saifur Rahman

  2. Bangladesh Army International University of Science and Technology, Cumilla Cantonment, Cumilla, Bangladesh

    Nargis Parvin

Authors
  1. Md. Saifur Rahman
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  2. Nargis Parvin
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Corresponding author

Correspondence to Md. Saifur Rahman .

Editor information

Editors and Affiliations

  1. Institute of Engineering, Tribhuvan University, Pulchowk Campus, Lalitpur, Nepal

    Subarna Shakya

  2. Intelligent Systems Research Centre, Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

  3. Songkla University, Songkhla, Thailand

    Sinchai Kamolphiwong

  4. Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada

    Ke-Lin Du

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Rahman, M.S., Parvin, N. (2022). Fundamental Frequency Extraction of Noisy Speech Using Exponent Enhancement in Weighted Autocorrelation. In: Shakya, S., Balas, V.E., Kamolphiwong, S., Du, KL. (eds) Sentimental Analysis and Deep Learning. Advances in Intelligent Systems and Computing, vol 1408. Springer, Singapore. https://doi.org/10.1007/978-981-16-5157-1_44

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