Engine noise separation through Gibbs sampling in a hierarchical Bayesian model. (1st August 2019)

• Record Type: Journal Article
• Authors: Brogna, G.; Antoni, J.; Leclere, Q.; Sauvage, O.
• Is Part Of: Mechanical systems and signal processing. Volume 128(2019)
• Page Start: 405
• Page End: 428
• Publication Date: 2019-08-01
• Subjects: Referenced source separation -- Bayesian computation -- Hierarchical modelling -- Engine noise; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2019.03.040
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: A Bayesian algorithm manages to separate sources using correlated references. In a Bayesian context, credibility intervals on the results are easily obtained. The proposed method is successfully compared to classic and cyclic Wiener filters. The mathematical relation between the proposed method and Wiener filters is shown. The algorithm converges alone toward the Wiener filter that gives the best results. Abstract: An algorithm based on a hierarchical Bayesian model is introduced to separate sources highly overlapping in time and frequency and observed through correlated references. The method is applied to internal combustion (IC) engine signals with the aim of separating the contributions due to different physical origins. The results are compared to the ones provided by classical Wiener filter. The Bayesian context allows correlated references to be taken into account with no consequences on the identifiability of the sources, thanks to the possibility of providing some regularizing prior information in the form of Bayesian prior laws. Moreover, the credibility interval on the estimated sources derives directly from the adopted sampling strategy. Finally, it is shown in a simple case that the proposed algorithm can be rewritten as a weighted sum of the classical and cyclic Wiener filters proposed by Pruvost in 2009. As opposed to them, the present algorithm autonomously chooses one or the other depending on the characteristics of the analysed signals. Even if the development context is the separation of the sources in an IC engine, the presented method is general and can be applied to any source separation problem.