Instantaneous Frequency Estimation In Multi-Component Signals Using Stochastic EM Algorithm

TL;DR

This paper introduces a Bayesian stochastic EM algorithm for accurate instantaneous frequency estimation in multi-component non-stationary signals contaminated with noise, demonstrating improved performance over existing methods.

Contribution

It presents a novel Bayesian model combined with a stochastic EM algorithm for efficient mode estimation in noisy, non-stationary signals, reducing computational complexity.

Findings

Enhanced frequency estimation accuracy

Outperforms state-of-the-art methods in experiments

Robust to noise and signal variability

Abstract

This paper addresses the problem of estimating the modes of an observed non-stationary mixture signal in the presence of an arbitrary distributed noise. A novel Bayesian model is introduced to estimate the model parameters from the spectrogram of the observed signal, by resorting to the stochastic version of the EM algorithm to avoid the computationally expensive joint parameters estimation from the posterior distribution. The proposed method is assessed through comparative experiments with state-of-the-art methods. The obtained results validate the proposed approach by highlighting an improvement of the modes estimation performance.