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 language | English |
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Title of host publication | The 2013 International Joint Conference on Neural Networks (IJCNN) |
Editors | Plamen Angelov, Daniel Levine, Bruno Apolloni |
Publisher | IEEE, Institute of Electrical and Electronics Engineers |
ISBN (Print) | 9781467361293, 9781467361286 |
DOIs | |
Publication status | Published - 2013 |
Externally published | Yes |
Event | 2013 IEEE International Joint Conference on Neural Networks - Dallas, United States Duration: 4 Aug 2013 → 9 Aug 2013 |
Conference
Conference | 2013 IEEE International Joint Conference on Neural Networks |
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Abbreviated title | IJCNN 2013 |
Country/Territory | United States |
City | Dallas |
Period | 4/08/13 → 9/08/13 |