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EvoComposer: An Evolutionary Algorithm for 4-Voice Music Compositions

机译:Evocomposer:一种用于4语音音乐组合物的进化算法

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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 theunfigured harmonization problem. The proposed algorithm for the unfigured harmonization problem, namedEvoComposer, 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 novelhybridmultiobjective 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 ofperformance, such as hypervolume, Delta index, coverage of two sets, and standard measures ofmusic creativity. We conjecture that a similar approach can be useful also for similar musical problems.
机译:进化算法模仿进化行为以解决问题。他们已成功应用于许多领域,似乎与创造性问题有一种特殊的关系;在过去的二十年中,这种关系导致了长期的应用程序,包括音乐领域的几个应用程序。在本文中,我们提供了一种能够撰写音乐的进化算法。更具体地说,我们考虑以下4语音协调问题:4个声音(是低音,男高音,Alto和Soprano)作为输入,作曲家必须编写其他3个声音,以便有一个完整的4 - 为每个输入注意为4票音符的音乐。解决这样的问题意味着找到用于每个输入注意的适当和弦,并且还发现每个和弦内的音符的放置,以便解决旋律问题。这样的问题被称为惩治协调问题。所提出的趋势统一问题的算法NamedVocomposer,在染色体方面使用了解决方案的新颖表示(这允许处理谐波和非谐波音调),专门的运营商(利用音乐信息以提高所生产的人的质量) ,以及一种新的ydbridMultiobjective评估功能(基于对Bach的音乐大语料库的原始统计分析)。此外,Evocomposer是该特定问题的第一进化算法。 Evocomposer是一种多目标进化算法,基于众所周知的NSGA-II战略,并考虑了两个目标:谐波目标,即寻找适当的和弦和旋律目标,该谐波具有找到合适的旋律线。作曲过程完全自动,没有任何人类干预。我们还提供了一种评价研究,表明Evocomposer通过在既有唯一可知的唯一可知的衡量标识,如超级玻璃,三角度指数,两组覆盖范围内生产更好的解决方案,和标准措施的创造力。我们猜想类似的方法也可用于类似的音乐问题。

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