Human usually composes music by organizing elements according to the musical form to express music ideas. However, for neural network-based music generation, it is difficult to do so due to the lack of labelled data on musical form. In this paper, we develop MeloForm, a system that generates melody with musical form using expert systems and neural networks. Specifically, 1) we design an expert system to generate a melody by developing musical elements from motifs to phrases then to sections with repetitions and variations according to pre-given musical form; 2) considering the generated melody is lack of musical richness, we design a Transformer based refinement model to improve the melody without changing its musical form. MeloForm enjoys the advantages of precise musical form control by expert systems and musical richness learning via neural models. Both subjective and objective experimental evaluations demonstrate that MeloForm generates melodies with precise musical form control with 97.79% accuracy, and outperforms baseline systems in terms of subjective evaluation score by 0.75, 0.50, 0.86 and 0.89 in structure, thematic, richness and overall quality, without any labelled musical form data. Besides, MeloForm can support various kinds of forms, such as verse and chorus form, rondo form, variational form, sonata form, etc.
翻译:人类通常根据音乐形式组织音乐,根据音乐形式组织音乐,以表达音乐思想。然而,由于缺少音乐形式上贴标签的数据,神经网络的音乐创作很难做到这一点。在本文中,我们开发了MeloForm,这是一个利用专家系统和神经网络以音乐形式产生旋律的系统。具体地说,我们设计了一个专家系统,通过开发音乐元素产生旋律,从motifs到根据音乐预发式形式对有重复和变异的章节进行曲调制;(2)考虑到所生成的旋律缺乏音乐丰富性,我们设计了一个基于变形器的改进模型,以便在不改变音乐形式的情况下改进旋律。MeloForm享有由专家系统进行精确音乐形式控制的优势,并通过神经模型学习音乐形式的音乐丰富性音乐。 主观和客观的实验性评估表明,MeloForm在以97.79%的精准度控制下生成了旋律形式,在主观评价得分为0.75、0.50、0.86和0.89的模型、主题、丰富性和整体质量方面,以及无任何定型形式,可以作为音乐形式的支持。