This article deals with low bitrate object coding of musical audio, and more precisely with the extraction of pitched sound objects in polyphonic music. After a brief review of existing methods, we discuss the potential benefits of recasting this problem in a Bayesian framework. We define pitched objects by a set of probabilistic priors and derive efficient algorithms to infer active objects and their parameters. Preliminary experiments suggest that the proposed method results in a better sound quality than simple sinusoidal coding while achieving a lower bitrate.