Speech enhancement by harmonic modeling via map pitch tracking

In this paper we present a procedure for estimating the parameters of speech signals that are contaminated by high level of noise. The proposed estimation method is developed by assuming a harmonic model for the voiced frame hypothesis. A Maximum A-posteriori Probability tracking method is developed for estimating time-varying pitch. Signal reconstruction is achieved by projecting the signal onto the subspace of harmonic signals with the optimal estimates of the fundamental frequency. The performance of the proposed method is evaluated and compared to other existing methods using a large pitch detection database. It is shown that the proposed method for pitch estimation is more robust and much more accurate in terms of mean-square-error and gross error rate, in comparison to other existing methods, specially at ultra low signal-to-noise ratios (as low as -15 dB). Examples of speech reconstruction/enhancement are also presented in the paper.