Autocorrelation of the Speech Multi-Scale Product for Voicing Decision and Pitch Estimation

In this work, we present an algorithm for voiced/unvoiced decision and pitch estimation from speech signals. Our approach is based on classifying the peaks provided by the autocorrelation of the speech multi-scale product. The multi-scale product is based on making the product of the speech wavelet transform coefficients at three successive dyadic scales. The autocorrelation function of the multi-scale product is calculated over frames of a specific length. The experimental results show the robustness and the effectiveness of our approach. Besides, the proposed method outperforms some existing algorithms in a clean and noisy environment.

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