Related papers: A framework to compare music generative models using automatic evaluation metrics extended to rhythm

A framework to compare music generative models using automatic evaluation metrics extended to rhythm

URL: http://arxiv.org/abs/2101.07669v1
Date: Tue, 19 Jan 2021 15:04:46 GMT
Title: A framework to compare music generative models using automatic evaluation metrics extended to rhythm
Authors: Sebastian Garcia-Valencia, Alejandro Betancourt, Juan G. Lalinde-Pulido
Abstract summary: This paper takes the framework proposed in a previous research that did not consider rhythm to make a series of design decisions, then, rhythm support is added to evaluate the performance of two RNN memory cells in the creation of monophonic music. The model considers the handling of music transposition and the framework evaluates the quality of the generated pieces using automatic quantitative metrics based on geometry which have rhythm support added as well.
Score: 69.2737664640826
License: http://creativecommons.org/licenses/by/4.0/
Abstract: To train a machine learning model is necessary to take numerous decisions about many options for each process involved, in the field of sequence generation and more specifically of music composition, the nature of the problem helps to narrow the options but at the same time, some other options appear for specific challenges. This paper takes the framework proposed in a previous research that did not consider rhythm to make a series of design decisions, then, rhythm support is added to evaluate the performance of two RNN memory cells in the creation of monophonic music. The model considers the handling of music transposition and the framework evaluates the quality of the generated pieces using automatic quantitative metrics based on geometry which have rhythm support added as well.

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