  In recent years, artificial neural networks (ANNs) have become a universal
tool for tackling real-world problems. ANNs have also shown great success in
music-related tasks including music summarization and classification,
similarity estimation, computer-aided or autonomous composition, and automatic
music analysis. As structure is a fundamental characteristic of Western music,
it plays a role in all these tasks. Some structural aspects are particularly
challenging to learn with current ANN architectures. This is especially true
for mid- and high-level self-similarity, tonal and rhythmic relationships. In
this thesis, I explore the application of ANNs to different aspects of musical
structure modeling, identify some challenges involved and propose strategies to
address them. First, using probability estimations of a Restricted Boltzmann
Machine (RBM), a probabilistic bottom-up approach to melody segmentation is
studied. Then, a top-down method for imposing a high-level structural template
in music generation is presented, which combines Gibbs sampling using a
convolutional RBM with gradient-descent optimization on the intermediate
solutions. Furthermore, I motivate the relevance of musical transformations in
structure modeling and show how a connectionist model, the Gated Autoencoder
(GAE), can be employed to learn transformations between musical fragments. For
learning transformations in sequences, I propose a special predictive training
of the GAE, which yields a representation of polyphonic music as a sequence of
intervals. Furthermore, the applicability of these interval representations to
a top-down discovery of repeated musical sections is shown. Finally, a
recurrent variant of the GAE is proposed, and its efficacy in music prediction
and modeling of low-level repetition structure is demonstrated.
