Music Generation Using an LSTM

• URL: http://arxiv.org/abs/2203.12105v1
• Date: Wed, 23 Mar 2022 00:13:41 GMT
• Title: Music Generation Using an LSTM
• Authors: Michael Conner, Lucas Gral, Kevin Adams, David Hunger, Reagan Strelow, and Alexander Neuwirth
• Abstract summary: Long Short-Term Memory (LSTM) network structures have proven to be very useful for making predictions for the next output in a series. We demonstrate an approach of music generation using Recurrent Neural Networks (RNN) We provide a brief synopsis of the intuition, theory, and application of LSTMs in music generation, develop and present the network we found to best achieve this goal, identify and address issues and challenges faced, and include potential future improvements for our network.
• Score: 52.77024349608834
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Over the past several years, deep learning for sequence modeling has grown in popularity. To achieve this goal, LSTM network structures have proven to be very useful for making predictions for the next output in a series. For instance, a smartphone predicting the next word of a text message could use an LSTM. We sought to demonstrate an approach of music generation using Recurrent Neural Networks (RNN). More specifically, a Long Short-Term Memory (LSTM) neural network. Generating music is a notoriously complicated task, whether handmade or generated, as there are a myriad of components involved. Taking this into account, we provide a brief synopsis of the intuition, theory, and application of LSTMs in music generation, develop and present the network we found to best achieve this goal, identify and address issues and challenges faced, and include potential future improvements for our network.

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