Spurred by the potential of deep learning, computational music generation has gained
renewed academic interest. A crucial issue in music generation is that of user control …

Deep learning approaches to automatic chord recognition of symbolic music have improved
the state of the art, but they still face a common problem: how to deal with a vast chord …

Machine learning allows automatic construction of generative models for music. However,
they are learned from only the succession of notes itself without explicitly employing domain …

Deep learning has given AI-based methods for music creation a boost by over the past
years. An important challenge in this field is to balance user control and autonomy in music …

Music streaming platforms rely heavily on learning meaningful representations of tracks to
surface apt recommendations to users in a number of different use cases. In this work, we …

Audio-to-score alignment is an important pre-processing step for in-depth analysis of
classical music. In this paper, we apply novel transposition-invariant audio features to this …

In this work, we study the use of convolutional neural networks for genre recognition in
symbolically represented music. Specifically, we explore the effects of changing network …

Learning features from data has shown to be more successful than using hand-crafted
features for many machine learning tasks. In music information retrieval (MIR), features …

Audio-to-score alignment aims at generating an accurate mapping between a performance
audio and the score of a given piece. Standard alignment methods are based on Dynamic …

We propose a new graph convolutional block, called MusGConv, specifically designed for
the efficient processing of musical score data and motivated by general perceptual …