Currently available reviews in the area of artificial intelligence-based music generation do
not provide a wide range of publications and are usually centered around comparing very …

Most existing neural network models for music generation use recurrent neural networks.
However, the recent WaveNet model proposed by DeepMind shows that convolutional …

Recent years have witnessed a surge in the popularity of Machine Learning (ML), applied
across diverse domains. However, progress is impeded by the scarcity of training data due …

Abstract We introduce the Generalized Discrimination Value (GDV) that measures, in a non-
invasive manner, how well different data classes separate in each given layer of an artificial …

There are many features that make music composition a challenging task for computer
science. many people are trying to generate music in different methods which generated …

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 …

Scramble is a standalone MIDI tool developed in Max/MSP for the real-time generation of
polyphonic music, that combines Markov chains and LSTM neural networks. It offers to the …

Advanced machine learning (ML) approaches such as transfer learning have seldom been
applied to approximate quantum many-body systems. Here we demonstrate that a simple …

This paper presents MahlerNet, a deep recurrent neural network that models polyphonic
music sequences of arbitrary length with an arbitrary number of instruments. The data …

The study scrutinizes the implementation of the Gated Recurrent Unit within Recurrent
Neural Networks for constructing nationalistic music. The GRU model demonstrates the …