A novel tool based on Bayesian filtering framework and expectation maximization algorithm is numerically and experimentally demonstrated for accurate frequency comb noise characterization. The tool is statistically optimum in a mean-square-error-sense works at wide range of SNRs and offers more accurate noise estimation compared to conventional methods.
