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Volume 5 , Issue 1 , June 2021 , Pages: 38 - 45
Reproducing Musicality: Immediate Human-like Musicality Through Machine Learning and Passing the Turing Test
Aran Samson, Department of Information Systems and Computer Science, Ateneo de Manila University, Quezon City, Philippines
Andrei Coronel, Department of Information Systems and Computer Science, Ateneo de Manila University, Quezon City, Philippines
Received: Apr. 17, 2021;       Accepted: May 12, 2021;       Published: May 26, 2021
DOI: 10.11648/j.ajai.20210501.13        View        Downloads  
Abstract
Musicology is a growing focus in computer science. Past research has had success in automatically generating music through learning-based agents that make use of neural networks and through model and rule-based approaches. These methods require a significant amount of information, either in the form of a large dataset for learning or a comprehensive set of rules based on musical concepts. This paper explores a model in which a minimal amount of musical information is needed to compose a desired style of music. This paper takes from two concepts, objectness, and evolutionary computation. The concept of objectness, an idea directly derived from imagery and pattern recognition, was used to extract specific musical objects from single musical inputs which are then used as the foundation to algorithmically produce musical pieces that are similar in style to the original inputs. These musical pieces are the product of evolutionary algorithms which implement a sequential evolution approach wherein a generated output may or may not yet be fully within the fitness thresholds of the input pieces. This method eliminates the need for a large amount of pre-provided data as well as the need for long processing times that are commonly associated with machine-learned art-pieces. This study aims to show a proof of concept of the implementation of the described model.
Keywords
Artificial Intelligence, Computer Music, Turing Testing
To cite this article
Aran Samson, Andrei Coronel, Reproducing Musicality: Immediate Human-like Musicality Through Machine Learning and Passing the Turing Test, American Journal of Artificial Intelligence. Vol. 5, No. 1, 2021, pp. 38-45. doi: 10.11648/j.ajai.20210501.13
Copyright
Copyright © 2021 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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