Computer-aided music composition with LSTM neural network and chaotic inspiration

A. E Coca, Debora Correa, L Zhao

Research output: Chapter in Book/Conference paperConference paperpeer-review

12 Citations (Scopus)

Abstract

In this paper a new neural network system for composition of melodies is proposed. The Long Short-Term Memory (LSTM) neural network is adopted as the neural network model. We include an independent melody as an additional input in order to provide an inspiration source to the network. This melody is given by a chaotic composition algorithm and works as an inspiration to the network enhancing the subjective measure of the composed melodies. As the chaotic system we use the Hénon map with two variables, which are mapped to pitch and rhythm. We adopt a measure to conduct the degree of melodiousness (Euler's gradus suavitatis) of the output melody, which is compared with a reference value. Varying a specific parameter of the chaotic system, we can control the complexity of the chaotic melody. The system runs until the degree of melodiousness falls within a predetermined range.
Original languageEnglish
Title of host publicationThe 2013 International Joint Conference on Neural Networks (IJCNN)
EditorsPlamen Angelov, Daniel Levine, Bruno Apolloni
PublisherIEEE, Institute of Electrical and Electronics Engineers
ISBN (Print)9781467361293, 9781467361286
DOIs
Publication statusPublished - 2013
Externally publishedYes
Event2013 IEEE International Joint Conference on Neural Networks - Dallas, United States
Duration: 4 Aug 20139 Aug 2013

Conference

Conference2013 IEEE International Joint Conference on Neural Networks
Abbreviated titleIJCNN 2013
Country/TerritoryUnited States
CityDallas
Period4/08/139/08/13

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