A Robust pitch estimation approach for clean speech

In this work, we present an algorithm for estimating the fundamental frequency in speech signals. Our approach is based on the spectral compression by the autocorrelation of the speech multi-scale product analysis. It consists of operating the product of compressed copies of the original spectrum on the multi-scale product autocorrelation. The multi-scale product is based on making the product of the speech wavelet transform coefficients at three successive dyadic scales. The wavelet used is the quadratic spline function with a support of 0.8 ms. We estimate the pitch for each time frame based on its multi-scale product autocorrelation of the harmonic product spectrum structure. We evaluate our approach on the Keele database. Experimental results show the effectiveness of our method presenting a good performance surpassing the other algorithms.