Approximate Kalman Filtering for the Harmonic plus Noise Model

Lucas Parra, Uday Jain

To appear at Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA01), Mohonk Mountain Resort, NY, 21-24 October 2001


We present a probabilistic description of the Harmonic plus Noise Model (HNM) for speech signals. This probabilistic formulation permits Maximum Likelihood (ML) parameter estimation and speech synthesis becomes a straightforward sampling from a distribution. It also permits development of a Kalman filter that tracks model parameters such as pitch, harmonic amplitudes, and auto-regressive coefficients. We focus here on pitch tracking for which the estimator is highly non-linear. As a result it is necessary to develop an approximate Kalman filter that goes beyond extended Kalman filtering.

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