In this paper we describe a model developed for the analysis of acoustic spectra. Unlike decompositions techniques that can result in difficult to interpret results this model explicitly models spectra as distributions and extracts sets of additive and semantically useful components that facilitate a variety of applications rang- ing from source separation, denoising, music transcription and sound recognition. This model is probabilistic in nature and is easily extended to produce sparse codes, and discover transform invariant components which can be optimized for particular applications.