EvoComposer: An Evolutionary Algorithm for 4-Voice Music Compositions

IF 4.6 2区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Evolutionary Computation Pub Date : 2020-09-02 DOI:10.1162/evco_a_00265

R. De Prisco;G. Zaccagnino;R. Zaccagnino

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引用次数: 15

Abstract

Evolutionary algorithms mimic evolutionary behaviors in order to solve problems. They have been successfully applied in many areas and appear to have a special relationship with creative problems; such a relationship, over the last two decades, has resulted in a long list of applications, including several in the field of music. In this article, we provide an evolutionary algorithm able to compose music. More specifically we consider the following 4-voice harmonization problem: one of the 4 voices (which are bass, tenor, alto, and soprano) is given as input and the composer has to write the other 3 voices in order to have a complete 4-voice piece of music with a 4-note chord for each input note. Solving such a problem means finding appropriate chords to use for each input note and also finding a placement of the notes within each chord so that melodic concerns are addressed. Such a problem is known as the unfigured harmonization problem. The proposed algorithm for the unfigured harmonization problem, named EvoComposer, uses a novel representation of the solutions in terms of chromosomes (that allows to handle both harmonic and nonharmonic tones), specialized operators (that exploit musical information to improve the quality of the produced individuals), and a novel hybrid multiobjective evaluation function (based on an original statistical analysis of a large corpus of Bach's music). Moreover EvoComposer is the first evolutionary algorithm for this specific problem. EvoComposer is a multiobjective evolutionary algorithm, based on the well-known NSGA-II strategy, and takes into consideration two objectives: the harmonic objective, that is finding appropriate chords, and the melodic objective, that is finding appropriate melodic lines. The composing process is totally automatic, without any human intervention. We also provide an evaluation study showing that EvoComposer outperforms other metaheuristics by producing better solutions in terms of both well-known measures of performance, such as hypervolume, Δ index, coverage of two sets, and standard measures of music creativity. We conjecture that a similar approach can be useful also for similar musical problems.

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EvoComposer:一个四声部音乐作品的进化算法

进化算法模仿进化行为来解决问题。它们已经成功地应用于许多领域，并且似乎与创造性问题有着特殊的关系;在过去的二十年里，这种关系产生了一长串的应用程序，包括音乐领域的一些应用程序。在本文中，我们提供了一个能够作曲的进化算法。更具体地说，我们考虑以下四声部和声问题:四声部中的一个(男低音、男高音、女高音和女高音)作为输入，作曲家必须写其他三个声部，以便有一个完整的四声部音乐作品，每个输入音符都有一个四音和弦。解决这样的问题意味着为每个输入音符找到合适的和弦，并在每个和弦中找到音符的位置，从而解决旋律问题。这样的问题被称为无图形协调问题。该算法被命名为EvoComposer，它使用了一种新的染色体解表示(允许处理谐波和非谐波音调)、专门的算子(利用音乐信息来提高产生的个体的质量)和一种新的混合多目标评估函数(基于对巴赫音乐大量语料库的原始统计分析)。此外，EvoComposer是针对这一特定问题的第一个进化算法。EvoComposer是一种基于NSGA-II策略的多目标进化算法，它考虑了两个目标:和声目标，即找到合适的和弦;旋律目标，即找到合适的旋律线。作曲过程是完全自动的，没有任何人为干预。我们还提供了一项评估研究，表明EvoComposer通过在众所周知的性能度量方面产生更好的解决方案，优于其他元启发式方法，例如hypervolume， Δ指数，两集的覆盖范围和音乐创造力的标准度量。我们推测，类似的方法也可以用于类似的音乐问题。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Evolutionary Computation 工程技术-计算机：理论方法

CiteScore

6.40

自引率

1.50%

发文量

审稿时长

3 months

期刊介绍： Evolutionary Computation is a leading journal in its field. It provides an international forum for facilitating and enhancing the exchange of information among researchers involved in both the theoretical and practical aspects of computational systems drawing their inspiration from nature, with particular emphasis on evolutionary models of computation such as genetic algorithms, evolutionary strategies, classifier systems, evolutionary programming, and genetic programming. It welcomes articles from related fields such as swarm intelligence (e.g. Ant Colony Optimization and Particle Swarm Optimization), and other nature-inspired computation paradigms (e.g. Artificial Immune Systems). As well as publishing articles describing theoretical and/or experimental work, the journal also welcomes application-focused papers describing breakthrough results in an application domain or methodological papers where the specificities of the real-world problem led to significant algorithmic improvements that could possibly be generalized to other areas.