Dictionary-Based Analysis/Synthesis and Structured Representations of Musical Audio

In the representation of musical audio, it is common to favour either a signal or symbol interpretation, where mid-level representation is an emerging topic. In this thesis we investigate the perspective of structured, intermediate representations through an integration of theoretical aspects related to separable sound objects, dictionary-based methods of signal analysis, and object-oriented programming. In contrast to examples in the literature that approach an intermediate representation from the signal level, we orient our formulation towards the symbolic level. This methodology is applied to both the specification of analytical techniques and the design of a software framework. Experimental results demonstrate that our method is able to achieve a lower Itakura-Saito distance, a perceptually-motivated measure of spectral dissimilarity, when compared to a generic model and that our structured representation can be applied to visualization as well as agglomerative post-processing.