Graph-based bayesian approach for transient interference suppression

In this paper, we present a method for transient interference suppression. The main idea is to learn the intrinsic geometric structure of the transients instead of relying on estimates of noise statistics. The transient interference structure is captured via a parametrization of a graph constructed from the measurements. This parametrization is viewed as an empirical model for transients and is used for building a filter that extracts transients from noisy speech. We present a model-based supervised algorithm, in which the graph-based empirical model is constructed in advance from training recordings, and then extended to new incoming measurements. This paper extends previous studies and presents a new Bayesian approach for empirical model extension that takes into account both the structure of the transients as well as the dynamics of speech signals.