Evaluation of pitch estimation in clean and noisy speech

  • Authors

    • A Satyanarayana Murthy
    • P Sairam
    • B Sai Kumar
    https://doi.org/10.14419/ijet.v7i3.29.19317
  • Auto-Correlation, Cestrum, Pitch, Speech Processing.

  • Abstract

    Every human being has a distinct voice due to pitch association and it is almost like a finger print. Pitch is one of the important parameter which is used in many speech processing applications. In reality speech is a complex combination of both voiced and unvoiced sounds and cannot be separated subjectively. For the voiced speech, pitch is defined as the rate of change of vocal folds vibrations. In practice, pitch is a subjective quantity and cannot be measured directly from the voice. It is a non-linear quantity, depends upon the spectral and temporal content of the signal. Many pitch estimation methods have been developed but none can work efficiently in the presence of additive noise. It is very essential to understand the effect of noise on the pitch estimation in dealing effectively with many speech processing applications. Speech processing systems should be robust enough to counter the presence of noise to produce good quality sounds. In non-intrusive speech quality measurement algorithms, pitch is one of the quality parameter for speech assessment. The accuracy of this feature in noisy speech is correlated with the subjective quality of speech. In this paper we have been evaluated the performance of auto-correlation and cepstrum algorithms for pitch estimation and tracking.

     

     

  • References

    1. [1] pitch detection algorithms for pathological voicesâ€, International Conference on Inventive Computational Technologiesâ€, Vol. 3, 26 August 2016, Coimbatore, India.

      [2] MNA Aadit, SG Kirtana, MT Mahin, “Pitch and formant estimation of bangle speech signal using auto-correlation, cesptrum and LPC algorithmâ€, 19th Itnernational Conference on Computer and Information Technology, 18-20 Dec. 2016, Dhaka, Bangladesh.

      [3] F. J. Harris, "On the Use of Windows for Harmonic Analysis with the Discrete Fourier Transform.'' Proceedings of the IEEE, vol. 66, no. 1, pp. 51¬84, 1978.

      [4] A. H. Nuttall, "Some Windows With Very Good Side lobe Behavior,'' IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 29, no. 1, pp. 84¬91, 1981.

      [5] M. M. Sondhi, "New Methods of Pitch Extraction,'' IEEE Trans. on Audio and Electroacoustic, vol. 16, no. 2, pp. 262¬266, 1968.

      [6] Kirill Sakhnov, Boris Simak, “Pitch detection algorithms and voiced/unvoiced classification for noisy speechâ€, 16th International Conference on System, Signals and Image Processingâ€, 28 Dec. 2009, Chalkida, Greece.

      [7] Y. M. Cheng and D. O'Shaughnessy, "Automatic and Reliable Estimation of Glottal Closure Instant and Period,'' IEEE Trans. on Acoustics, Speech, and Signal Processing, vol. 37, no. 12, pp. 1805¬1815, 1989.

      [8] Cansu, Seda, Yard, “A study on pitch detection algorithmsâ€, 23rd Conference on Signal Processing and Communications Applicationsâ€, 16-19 May 2015, Malatya, Turkey.

      [9] NOIZEUS: A noisy speech corpus for evaluation of speech enhancement algorithms

      [10] P. R. Cook, "An Automatic Pitch Detection and MIDI Control System for Brass Instruments," Stanford Center for Computer Research in Music and Acoustics.

      [11] T. V. Ananthapadmanabha and B. Yegnanarayana, "Epoch Extraction from Linear Prediction Residual for Identification of Closed Glottis Interval,'' IEEE Trans. on Acoustics, Speech, and Signal Processing, vol. 27, no. 4, pp. 309¬319, 1979.

      [12] J. F. Deem, W. H. Manning, J. V. Knack and J. S. Matesich, "The Automatic Extraction of Pitch Perturbation Using Microcomputers: Some Methodological Considerations,'' Journal of Speech and Hearing Research, vol. 32, pp. 689-697, 1989.

      [13] H. Chamberlin, "Musical Applications of Microprocessors". New Jersey: Hayden Book Company, 1980.

      [14] J. M. Cioffi, "Limited Precision Effects in Adaptive Filtering,'' IEEE Transactions on Circuits and Systems, vol. 34, no. 7, pp. 821¬833, 1987.

      [15] M. J. Ross, H. L. Shaffer, A. Cohen, R. Freudberg and H. J. Manley, "Average Magnitude Difference Function Pitch Extractor,'' IEEE Trans. on Acoustics, Speech and Signal Processing, vol. 22, no. 5, pp. 353¬362, 1974.

      [16] R. L. Miller and E. S. Weibel, "Measurements of the Fundamental Period of Speech Using a Delay Line,'' Journal of the Acoustical Society of America, vol. 28, Abstract, 1956.

      [17] A. Moorer, "The Optimum Comb Method of Pitch Period Analysis of Continuous Digitized Speech,'' IEEE Trans. on Acoustics, Speech, and Signal Processing, vol. 22, no. 5, pp. 330¬338, 1974.

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  • How to Cite

    Satyanarayana Murthy, A., Sairam, P., & Sai Kumar, B. (2018). Evaluation of pitch estimation in clean and noisy speech. International Journal of Engineering & Technology, 7(3.29), 580-585. https://doi.org/10.14419/ijet.v7i3.29.19317

    Received date: 2018-09-09

    Accepted date: 2018-09-09