Bach in 2014: Music Composition with Recurrent Neural Network

Indiana Undergraduate Journal of

Artificial neural networks have been used to model many aspects of human-music interaction. Pitch perception, key identification, melody discrimination, and original composition are all tasks for which researchers have tried to use networks. For this project, however, I was most interested in the idea of using a neural network for harmonization. Since (good) harmonization is considered a diffic...

A New Recurrent Fuzzy Neural Network Controller Design for Speed and Exhaust Temperature of a Gas Turbine Power Plant

In this paper, a recurrent fuzzy-neural network (RFNN) controller with neural network identifier in direct control model is designed to control the speed and exhaust temperature of the gas turbine in a combined cycle power plant. Since the turbine operation in combined cycle unit is considered, speed and exhaust temperature of the gas turbine should be simultaneously controlled by fuel command ...

Music Composition Using Recurrent Neural Networks and Evolutionary Algorithms

Predicting Missing Music Components with Bidirectional Long Short-Term Memory Neural Networks

Successfully predicting missing components (entire parts or voices) from complex multipart musical textures has attracted researchers of music information retrieval and music theory. However, these applications were limited to either two-part melody and accompaniment (MA) textures or four-part Soprano-Alto-Tenor-Bass (SATB) textures. This paper proposes a robust framework applicable to both tex...

A First Look at Music Composition using LSTM Recurrent Neural Networks

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 reproduce phrases, attempts at learning an entire musical form and using that knowledge to guide composition have been unsuccessful. The reason for this failure seems to be that RNNs cannot keep track of temporally distan...