Pseudo-Bayesian Approach for Robust Mode Detection and Extraction Based on the STFT

Abstract

This paper addresses the problem of disentangling nonoverlapping multicomponent signals from their observation being possibly contaminated by external additive noise. We aim to extract and to retrieve the elementary components (also called modes) present in an observed nonstationary mixture signal. To this end, we propose a new pseudo-Bayesian algorithm to perform the estimation of the instantaneous frequency of the signal modes from their time-frequency representation. In a second time, a detection algorithm is developed to restrict the time region where each signal component behaves, to enhance quality of the reconstructed signal. We finally deal with the presence of noise in the vicinity of the estimated instantaneous frequency by introducing a new reconstruction approach relying on nonbinary band-pass synthesis filters. We validate our methods by comparing their reconstruction performance to state-of-the-art approaches through several experiments involving both synthetic and real-world data under different experimental conditions.

Benchmarks of Multi-Component Signal Analysis Methods

“…The pseudo-Bayesian (PB) method [7] addresses the IF estimation problem, by detecting the ridges on the TF plane within a Bayesian framework. The spectrogram of the observed signal is modeled as a random variable assuming the presence of a unique component at each TF point, as:…”