Reproducing Musicality: Immediate Human-like Musicality Through Machine Learning and Passing the Turing Test

Aran Samson; Andrei Coronel

doi:doi:10.11648/j.ajai.20210501.13

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Reproducing Musicality: Immediate Human-like Musicality Through Machine Learning and Passing the Turing Test

Aran Samson, Andrei Coronel

Published in American Journal of Artificial Intelligence (Volume 5, Issue 1)

Received: 17 April 2021 Accepted: 12 May 2021 Published: 26 May 2021

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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.

Published in	American Journal of Artificial Intelligence (Volume 5, Issue 1)
DOI	10.11648/j.ajai.20210501.13
Page(s)	38-45
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2021. Published by Science Publishing Group

Keywords

Artificial Intelligence, Computer Music, Turing Testing

References

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[12]	Samson, A. V., & Coronel, A. D. (2019, February). Reproducing Musicality: Detecting Musical Objects and Emulating Musicality through Partial Evolution. In Intelligence in Information and Communication (ICAIIC).
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Aran Samson, Andrei Coronel. (2021). Reproducing Musicality: Immediate Human-like Musicality Through Machine Learning and Passing the Turing Test. American Journal of Artificial Intelligence, 5(1), 38-45. https://doi.org/10.11648/j.ajai.20210501.13

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ACS Style

Aran Samson; Andrei Coronel. Reproducing Musicality: Immediate Human-like Musicality Through Machine Learning and Passing the Turing Test. Am. J. Artif. Intell. 2021, 5(1), 38-45. doi: 10.11648/j.ajai.20210501.13

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AMA Style

Aran Samson, Andrei Coronel. Reproducing Musicality: Immediate Human-like Musicality Through Machine Learning and Passing the Turing Test. Am J Artif Intell. 2021;5(1):38-45. doi: 10.11648/j.ajai.20210501.13

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@article{10.11648/j.ajai.20210501.13,
  author = {Aran Samson and Andrei Coronel},
  title = {Reproducing Musicality: Immediate Human-like Musicality Through Machine Learning and Passing the Turing Test},
  journal = {American Journal of Artificial Intelligence},
  volume = {5},
  number = {1},
  pages = {38-45},
  doi = {10.11648/j.ajai.20210501.13},
  url = {https://doi.org/10.11648/j.ajai.20210501.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajai.20210501.13},
  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.},
 year = {2021}
}

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TY  - JOUR
T1  - Reproducing Musicality: Immediate Human-like Musicality Through Machine Learning and Passing the Turing Test
AU  - Aran Samson
AU  - Andrei Coronel
Y1  - 2021/05/26
PY  - 2021
N1  - https://doi.org/10.11648/j.ajai.20210501.13
DO  - 10.11648/j.ajai.20210501.13
T2  - American Journal of Artificial Intelligence
JF  - American Journal of Artificial Intelligence
JO  - American Journal of Artificial Intelligence
SP  - 38
EP  - 45
PB  - Science Publishing Group
SN  - 2639-9733
UR  - https://doi.org/10.11648/j.ajai.20210501.13
AB  - 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.
VL  - 5
IS  - 1
ER  -

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Author Information

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

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Plain Text BibTeX RIS

APA Style

Aran Samson, Andrei Coronel. (2021). Reproducing Musicality: Immediate Human-like Musicality Through Machine Learning and Passing the Turing Test. American Journal of Artificial Intelligence, 5(1), 38-45. https://doi.org/10.11648/j.ajai.20210501.13

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ACS Style

Aran Samson; Andrei Coronel. Reproducing Musicality: Immediate Human-like Musicality Through Machine Learning and Passing the Turing Test. Am. J. Artif. Intell. 2021, 5(1), 38-45. doi: 10.11648/j.ajai.20210501.13

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AMA Style

Aran Samson, Andrei Coronel. Reproducing Musicality: Immediate Human-like Musicality Through Machine Learning and Passing the Turing Test. Am J Artif Intell. 2021;5(1):38-45. doi: 10.11648/j.ajai.20210501.13

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@article{10.11648/j.ajai.20210501.13,
  author = {Aran Samson and Andrei Coronel},
  title = {Reproducing Musicality: Immediate Human-like Musicality Through Machine Learning and Passing the Turing Test},
  journal = {American Journal of Artificial Intelligence},
  volume = {5},
  number = {1},
  pages = {38-45},
  doi = {10.11648/j.ajai.20210501.13},
  url = {https://doi.org/10.11648/j.ajai.20210501.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajai.20210501.13},
  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.},
 year = {2021}
}

Copy | Download

TY  - JOUR
T1  - Reproducing Musicality: Immediate Human-like Musicality Through Machine Learning and Passing the Turing Test
AU  - Aran Samson
AU  - Andrei Coronel
Y1  - 2021/05/26
PY  - 2021
N1  - https://doi.org/10.11648/j.ajai.20210501.13
DO  - 10.11648/j.ajai.20210501.13
T2  - American Journal of Artificial Intelligence
JF  - American Journal of Artificial Intelligence
JO  - American Journal of Artificial Intelligence
SP  - 38
EP  - 45
PB  - Science Publishing Group
SN  - 2639-9733
UR  - https://doi.org/10.11648/j.ajai.20210501.13
AB  - 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.
VL  - 5
IS  - 1
ER  -

Copy | Download