Instantaneous Frequency Estimation In Multi-Component Signals Using Stochastic EM Algorithm
This work addresses a domain-specific problem in signal processing for applications like audio or biomedical analysis, but it appears incremental as it builds on existing methods with a focus on computational efficiency.
The paper tackles the problem of estimating modes in non-stationary mixture signals with arbitrary noise by introducing a novel Bayesian model that uses the stochastic EM algorithm to avoid costly joint parameter estimation, resulting in improved mode estimation performance as validated in comparative experiments.
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.