Discriminative Training of Hidden Markov Models for Multiple Pitch Tracking

We present a multiple pitch tracking algorithm that is based on direct probabilistic modeling of the spectrogram of the signal. The model is a factorial hidden Markov model whose parameters are learned discriminatively from the Keele pitch database. Our algorithm can track several pitches and determines the number of pitches that are active at any given time. We present simulation results on mixtures of several speech signals and noise, showing the robustness of our approach.