Learning to Generate Music With Sentiment

Deep Learning models have shown very promising results in automatically composing polyphonic music pieces. However, it is very hard to control such models in order to guide the compositions towards a desired goal. We are interested in controlling a model to automatically generate music with a given...

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Bibliographic Details
Published in:arXiv.org 2021-03
Main Authors: Ferreira, Lucas N, Whitehead, Jim
Format: Article
Language:English
Subjects:
Computer & video games
Data mining
Deep learning
Machine learning
Model accuracy
Music
Sentiment analysis
Sound tracks
Video data
Online Access:Get full text
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Summary:Deep Learning models have shown very promising results in automatically composing polyphonic music pieces. However, it is very hard to control such models in order to guide the compositions towards a desired goal. We are interested in controlling a model to automatically generate music with a given sentiment. This paper presents a generative Deep Learning model that can be directed to compose music with a given sentiment. Besides music generation, the same model can be used for sentiment analysis of symbolic music. We evaluate the accuracy of the model in classifying sentiment of symbolic music using a new dataset of video game soundtracks. Results show that our model is able to obtain good prediction accuracy. A user study shows that human subjects agreed that the generated music has the intended sentiment, however negative pieces can be ambiguous.
ISSN:2331-8422