## Bayesian Modelling of Music: Algorithmic Advances and . . . (2005)

### BibTeX

@MISC{Blumensath05bayesianmodelling,

author = {Thomas Blumensath},

title = {Bayesian Modelling of Music: Algorithmic Advances and . . . },

year = {2005}

}

### OpenURL

### Abstract

In order to perform many signal processing tasks such as classification, pattern recognition and coding, it is helpful to specify a signal model in terms of meaningful signal structures. In general, designing such a model is complicated and for many signals it is not feasible to specify the appropriate structure. Adaptive models overcome this problem by learning structures from a set of signals. Such adaptive models need to be general enough, so that they can represent relevant structures. However, more general models often require additional constraints to guide the learning procedure. In this thesis