IMPROVED HIDDEN MARKOV MODEL PARTIAL TRACKING THROUGH TIME-FREQUENCY ANALYSIS

In this article we propose a modification to the combinatorial hidden Markov model developed in [1] for tracking partial frequency trajectories. We employ the Wigner-Ville distribution and Hough transform in order to (re)estimate the frequency and chirp rate of partials in each analysis frame. We estimate the initial phase and amplitude of each partial by minimizing the squared error in the time-domain. We then formulate a new scoring criterion for the hidden Markov model which makes the tracker more robust for non-stationary and noisy signals. We achieve good performance tracking crossing linear chirps and crossing FM signals in white noise as well as real instrument recordings.

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