Stimulus design for auditory neuroethology using state space modeling and the extended Kalman smoother


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Abstract

A new method for designing vocalization based stimuli for experiments in auditory neurophysiology is described. This analysis-synthesis technique leverages a state space statistical signal model and the extended Kalman smoother for tracking the frequency, amplitude, and phase information of harmonically related components in recorded vocalizations. Using the same state space model, these parameters can then be used to synthesize the vocalizations and random or deterministic variants of the vocalizations. This method is shown to outperform short-time Fourier transform based frequency tracking methods in both noisy and noise-free synthetic test signals. It is further shown to accurately track recorded hummingbird, human, and bat vocalizations while removing recording artifacts such as noise, echo, and digital aliasing in the synthesis phase.

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