Aero-acoustics source separation with sparsity inducing priors in the frequency domain

Olivier Schwander (1) , José Picheral (1) , Nicolas Gac (2) , Ali-Mohammad Djafari (2) , Daniel Blacodon (3)

1 E3S - Supélec Sciences des Systèmes
2 L2S - Laboratoire des signaux et systèmes
3 ONERA - The French Aerospace Lab [Châtillon]

The characterization of acoustic sources is of great interest in many industrial applications, in particular for the aeronautic or automotive industry for the development of new products. While localization of sources using observations from a wind tunnel is a well-known subject, the characterization and separation of the sources still needs to be explored. We present here a Bayesian approach for sources separation. Two prior modeling of the sources are considered: a sparsity inducing prior in the frequency domain and an auto-regressive model in the time domain. The proposed methods are evaluated on synthetic data simulating noise sources emitting from an airfoil inside a wind tunnel.

Keywords: Sparsity; Joint Maximum A Posteriori (JMAP); Bayesian Source Separation; Auto-regressive prior; Windtunnel

Domains: Engineering Sciences [physics]; Signal and Image processing

Olivier Schwander, José Picheral, Nicolas Gac, Ali-Mohammad Djafari, Daniel Blacodon. Aero-acoustics source separation with sparsity inducing priors in the frequency domain. 34th International Workshop on Bayesian Inference and Maximun Entropy Methods in Science and Engineering (MaxEnt'14), Sep 2014, Amboise, France. pp.422 - 431, ⟨10.1063/1.4906006⟩. ⟨hal-01103779⟩