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 …

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

In this paper, we introduce new methods and discuss results of text-based LSTM (Long
Short-Term Memory) networks for automatic music composition. The proposed network is …

In general music composed by recurrent neural networks (RNNs) suffers from a lack of
global structure. Though networks can learn note-by-note transition probabilities and even …

We consider the problem of extracting essential ingredients of music signals, such as a well-
defined global temporal structure in the form of nested periodicities (or meter). We …

Long Short-Term Memory (LSTM) neural networks have been effectively applied on learning
and generating musical sequences, powered by sophisticated musical representations and …

In this paper, we propose a generic technique to model temporal dependencies and
sequences using a combination of a recurrent neural network and a Deep Belief Network …

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 …

A big challenge in algorithmic composition is to devise a model that is both easily trainable
and able to reproduce the long-range temporal dependencies typical of music. Here we …

In the domain of algorithmic music composition, machine learning-driven systems eliminate
the need for carefully hand-crafting rules for composition. In particular, the capability of …