Structure is one of the most essential aspects of music, and music structure is commonly indicated through repetition. However, the nature of repetition and structure in music is still not well understood, especially in the context of music generation, and much remains to be explored with Music Information Retrieval (MIR) techniques. Analyses of two popular music datasets (Chinese and American) illustrate important music construction principles: (1) structure exists at multiple hierarchical levels, (2) songs use repetition and limited vocabulary so that individual songs do not follow general statistics of song collections, (3) structure interacts with rhythm, melody, harmony, and predictability, and (4) over the course of a song, repetition is not random, but follows a general trend as revealed by cross-entropy. These and other findings offer challenges as well as opportunities for deep-learning music generation and suggest new formal music criteria and evaluation methods. Music from recent music generation systems is analyzed and compared to human-composed music in our datasets, often revealing striking differences from a structural perspective.
翻译:音乐结构是音乐最重要的方面之一,音乐结构通常通过重复来显示,但是,音乐重复和结构的性质仍然没有得到很好地理解,特别是在音乐制作方面,还有许多有待与音乐信息检索技术探讨。对两个流行音乐数据集(中美)的分析说明了重要的音乐制作原则:(1) 结构存在于多个等级层次,(2) 歌曲使用重复和词汇有限,使个别歌曲不遵循歌集的一般统计,(3) 结构与节奏、旋律、和谐和可预测性相互作用,(4) 歌曲过程中的结构互动,重复不是随机的,而是遵循交叉流行所揭示的一般趋势。这些和其他调查结果为深造音乐提供了挑战,也提供了机会,提出了新的正式音乐标准和评价方法。对最近音乐生成系统的音乐进行了分析,并与我们数据集中的人造音乐进行了比较,常常从结构角度揭示出惊人的差异。