Abstract
Choosing a pitch estimation algorithm is not a simple task. One must balance between the accuracy and the reliability of the estimates. Two classes of methods are available. The first one, known as the “block methods” class, gives noise robust solutions and has an intrinsic averaging property, but is not very accurate, especially for the transition regions. The second one, known as the “instantaneous (or event-based) methods” class, gives very accurate estimates, but is considered to be inadequate in the presence of noise.
In this paper, we present potential enhancements of the performance in pitch estimation, based on both block and instantaneous methods. In this respect we discuss mainly two algorithms: a nonlinear cepstral algorithm and a wavelet-based one. The first algorithm, due to the proposed nonlinear model, enhances the classical linear model performance related to the accuracy of the estimated pitch for the transition regions and to the robustness in the presence of noise. Concerning the second algorithm, to the inherent accuracy of the estimated pitch, we add robust estimates even in the presence of noise, based on the multiresolution properties of an improved wavelet transform. The obtained enhancements were evaluated on a hand-labeled speech database, and the improved algorithms are now being applied in our research concerning speech compression and prosody.
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Gavat, I., Zirra, M. & Sabac, B. Pitch Estimation by Block and Instantaneous Methods. International Journal of Speech Technology 5, 269–279 (2002). https://doi.org/10.1023/A:1020201125377
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DOI: https://doi.org/10.1023/A:1020201125377