This paper describes a predominant pitch extraction system based on a family of Bayesian harmonic models. These models represent the short term waveform of the observed signal as a sum of harmonic partials and a residual noise. The amplitudes of the partials are modelled by a prior learnt on a training set, whereas the residual is modelled by a psycho-acoustically motivated prior. Efficient algorithms are provided to estimate the best fundamental frequency according to the MAP criterion. The performance of the method is evaluated in the framework of MIREX 2005.