  Predictive models for music are studied by researchers of algorithmic
composition, the cognitive sciences and machine learning. They serve as base
models for composition, can simulate human prediction and provide a
multidisciplinary application domain for learning algorithms. A particularly
well established and constantly advanced subtask is the prediction of
monophonic melodies. As melodies typically involve non-Markovian dependencies
their prediction requires a capable learning algorithm. In this thesis, I apply
the recent feature discovery and learning method PULSE to the realm of symbolic
music modeling. PULSE is comprised of a feature generating operation and
L1-regularized optimization. These are used to iteratively expand and cull the
feature set, effectively exploring feature spaces that are too large for common
feature selection approaches. I design a general Python framework for PULSE,
propose task-optimized feature generating operations and various
music-theoretically motivated features that are evaluated on a standard corpus
of monophonic folk and chorale melodies. The proposed method significantly
outperforms comparable state-of-the-art models. I further discuss the free
parameters of the learning algorithm and analyze the feature composition of the
learned models. The models learned by PULSE afford an easy inspection and are
musicologically interpreted for the first time.
