Evolutionary algorithm-based composition of hybrid-genre melodies using selected feature sets

Samson, Aran; Coronel, Andrei D.

doi:10.1109/iwcia.2016.7805748

Cited by 3 publications

(2 citation statements)

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“…The algorithmic approach in musical composition is taken directly from previous research done in evolutionary algorithmic musical composition [4,16,17]. The implementation in this study necessitated the use of the elements discussed in the previous section and was directly applied to the musical composition system in [4]. Previous research done [12] illustrates a proof of concept that the use of this method is promising in achieving the goals of this study.…”

Section: Algorithms and Methodsmentioning

confidence: 99%

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

Samson¹,

Coronel²

2021

AJAI

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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.

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Section: Algorithms and Methodsmentioning

confidence: 99%

“…To assess the similarity of the overall style of the output musical pieces, musical features are extracted from both the original piece and the algorithmically composed musical pieces and distance is compared between feature values. This method has been used in prior research in musical composition [4].…”

Section: Introductionmentioning

confidence: 99%