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 …

We propose an automatic music generation demo based on artificial neural networks, which
integrates the ability of Long Short-Term Memory (LSTM) in memorizing and retrieving …

Our goal is to be able to build a generative model from a deep neural network architecture to
try to create music that has both harmony and melody and is passable as music composed …

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

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 …

Since the advent of deep learning, it has been used to solve various problems using many
different architectures. The application of such deep architectures to auditory data is also not …

This paper presents a deep-learning architecture which is capable of modelling signatures
that represent the rhythm of music recordings. The proposed architecture consists of a stack …

We propose an end-to-end approach for modeling polyphonic music with a novel graphical
representation, based on music theory, in a deep neural network. Despite the success of …

We describe a neural network architecture which enables prediction and composition of
polyphonic music in a manner that preserves translation-invariance of the dataset …

Recurrent neural networks (RNNs) are now widely used on sequence generation tasks due
to their ability to learn long-range dependencies and to generate sequences of arbitrary …