Recent applications of Transformer neural networks in the field of music have demonstrated
their ability to effectively capture and emulate long-term dependencies characteristic of …

Considering music as a sequence of events with multiple complex dependencies, the Long
Short-Term Memory (LSTM) architecture has proven very efficient in learning and …

Abstract Machine learning has shown a successful component of methods for automatic
music composition. Considering music as a sequence of events with multiple complex …

The automatic composition of music with long-term structure is a central problem in music
generation. Neural network-based models have been shown to perform relatively well in …

We explore the transformer neural network architecture for modeling music, specifically Irish
and Swedish traditional dance music. Given the repetitive structures of these kinds of music …

Algorithmic music composition has long been in the spotlight of music information research
and Long Short-Term Memory (LSTM) neural networks have been extensively used for this …

This dissertation presents ten studies focusing on three important subfields of music
information retrieval (MIR): music transcription (Part A), music perception (Part B), and music …

Recurrent (neural) networks have been deployed as models for learning musical processes,
by computational scientists who study processes such as dynamic systems. Over time, more …

Music generation has generally been focused on either creating scores or interpreting them.
We discuss differences between these two problems and propose that, in fact, it may be …

In this paper we present R-VAE, a system designed for the exploration of latent spaces of
musical rhythms. Unlike most previous work in rhythm modeling, R-VAE can be trained with …