International Journal of Speech Technology

, Volume 22, Issue 4, pp 885–892 | Cite as

Performance measurement of a novel pitch detection scheme based on weighted autocorrelation for speech signals

  • Sandeep KumarEmail author


A novel pitch detection scheme (PDS) based on weighted autocorrelation function (WACF) is proposed. The proposed scheme has been simulated and then integrated in an analysis-synthesis system for speech signal. The simulation and real-time performance comparison of this scheme with two other existing schemes [ACF and weighted ACF (WACF)] has been carried out. The performance comparison results show that the proposed PDS outperforms (in terms of speech quality and intelligibility) for both clean as well as noisy environment as compared to the other conventional PDS schemes considered for the comparison. Moreover, simulation and real-time implementation results show that the time taken for computation and memory consumption for the proposed PDS is less as compared to the weighted ACF based PDS.


Signal processing Pitch detection ACF WACF 



  1. Bhattacharya, S., Singh, S. K., & Abhinav, T. (2012). Performance evaluation of LPC and Cepstral speech coder in simulation and in real-time. In Proceedings of IEEE International Conference on Recent Advances in Information Technology (RAIT) (826–831).Google Scholar
  2. Deller, J. R., Hansen, J. H. L., & Proakis, J. G. (2000). Discrete-time processing of speech signal (pp. 570–579). Hoboken: Wiley.Google Scholar
  3. Fangming, W., & Yip, P. (1991). Cepstral analysis using discrete trignomatric transform. IEEE Transactions Acoustics, Speech, Signal Processing (ASSP),39(2), 538–541.CrossRefGoogle Scholar
  4. Han, Z., & Wang, X. (2019). A signal period detection algorithm based on morphological self-complementtary top-hat transform and AMDF. Information,10(1), 1–12.CrossRefGoogle Scholar
  5. Hu, Y., & Loizou, P. (2007). Subjective evaluation and comparison of speech enhancement algorithms. Speech Communication,49, 588–601.CrossRefGoogle Scholar
  6. Huang, H., & Pan, J. (2006). Speech pitch determination based on Huang-Hilbert transform. Signal Processing,86(4), 792–803.CrossRefGoogle Scholar
  7. Kumar, B. (2018). Comparative performance evaluation of MMSE-based speech enhancement techniques through simulation and real-time implementation. International Journal of Speech Technology, Springer.,21(4), 1033–1044.CrossRefGoogle Scholar
  8. Kumar, S., Bhattacharya, S., Dhiman, V., & Mohapatra, S. (2013). Performance evaluation of a wavelet-based pitch detection scheme. International Journal of Speech Technology, Springer.,16(4), 431–437.CrossRefGoogle Scholar
  9. Kumar, S., Bhattacharya, S., & Patel, P. (2014). A new pitch detection scheme based on ACF and AMDF. In Proceedings of IEEE International Conference on Advanced Communications, Control and Computing Technologies, Ramnathapuram, India (pp. 1235–1240).Google Scholar
  10. Kumar, S., Bhattacharya, S., & Singh, S. K. (2015). Performance evaluation of a ACF-AMDF based pitch detection in real-time. International Journal of Speech Technology, Springer.,18(4), 521–527.CrossRefGoogle Scholar
  11. Mohapatra, S., Dhiman, V., Kumar, S., & Bhattacharya, S. (2011). A theoretical Justification for coincidence of wavelet maxima at a particular scale pair in an Event-based pitch detection method, In Proceedings of IEEE International Conference on Devices Communications (ICDeCom), BIT Mesra, Ranchi (India) (pp. 403–406).Google Scholar
  12. Perceptual Evaluation of Speech Quality (PESQ), ITU-T P. 862.Google Scholar
  13. Pirker, G., Wohlmayr, M., Petrik, S., & Pernkopf, F. (2011). A pitch tracking corpus with evaluation on multipitch tracking scenario. Interspeech, 1509–1512.Google Scholar
  14. Plante, F., Ainsworth, William A., & Meyer, Georg F. (1995). A pitch extraction reference database. EUROSPEECH,95, 837–840.Google Scholar
  15. Rabiner, L. R. (1977). On the use of autocorrelation analysis for pitch detection. IEEE Transactions Acoustics,Speech, Signal Processing (ASSP),25(1), 24–33.CrossRefGoogle Scholar
  16. Ross, M. J., Shaffer, H. L., Cohen, A., Freudberg, R., & Manley, H. J. (1974). Average magnitude difference function pitch extractor. IEEE Transactions Acoustics, Speech, Signal Processing (ASSP),22(5), 353–362.CrossRefGoogle Scholar
  17. Shimamura, T., & Kobayashi, H. (2001). Weighted autocorrelation for pitch extraction of noisy speech. IEEE Transactions on Speech and Audio Processing,9(7), 727–730.CrossRefGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Electronics & Communication EngineeringNational Institute of TechnologyDelhiIndia

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