We apply deep learning methods, specifically long short-term memory (LSTM) networks, to
music transcription modelling and composition. We build and train LSTM networks using …

This paper aims to propose a hierarchical, generative account of diatonic harmonic
progressions and suggest a set of phrase-structure grammar rules. It argues that the …

Within the framework of statistical learning, many behavioural studies investigated the
processing of unpredicted events. However, surprisingly few neurophysiological studies are …

We introduce a method for imposing higher-level structure on generated, polyphonic music.
A Convolutional Restricted Boltzmann Machine (C-RBM) as a generative model is combined …

The field of computational creativity, including musical metacreation, strives to develop
artificial systems that are capable of demonstrating creative behavior or producing creative …

Despite the recent upsurge of interest in the investigation of creativity, the question of how to
measure creativity is arguably underdiscussed. The aim of this paper is to address this gap …

We survey the history of studies of Computational Creativity, following the development of
the International Conference on Computational Creativity from its beginnings, a decade ago …

Musical expertise entails meticulous stylistic specialisation and enculturation. Even so,
research on musical training effects has focused on generalised comparisons between …

This paper has two different aims: presenting the problem of target-based computer-assisted
orchestration and introducing a new computational framework to solve it, called Orchidea …

We present a computational framework for chord invention based on a cognitive-theoretic
perspective on conceptual blending. The framework builds on algebraic specifications, and …