Artificial Music Generation using LSTM Networks
Hemalatha Eedi

Hemalatha Eedi*, Assistant Professor, Department of Computer Science and Engineering, JNTUH College of Engineering Hyderabad, Hyderabad, India.

Manuscript received on November 22, 2019. | Revised Manuscript received on December 15, 2019. | Manuscript published on December 30, 2019. | PP: 4315-4319 | Volume-9 Issue-2, December, 2019. | Retrieval Number: B4522129219/2019©BEIESP | DOI: 10.35940/ijeat.B4522.129219
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Abstract: Advancements in machine learning have minimized the gap of variation between human and algorithm composed music. This paper realizes a music generation system using evolutionary algorithms. The music generation is fully automated with no requirement of human intervention. Multiple music sample from a single dataset were used to the neural network. Software has been constructed to exhibit the results over various datasets. The proposed model is based on recurrent neural network with the input layer represents a measure at time T, and the output layer represents the measure at time T+1. The approach results in generation of new music composition by the system. Composition rules are used as constraints to evaluate the melodies generated by the novel neural network. Thus, the results are expected to evolve to satisfy the defined constraints. The proposed system of work would be capable of music generation without human intervention.
Keywords: Machine Learning, Music Generation, Recurrent Neural Networks.