Intelligent music generation, one of the most popular subfields of computer creativity, can lower the creative threshold for non-specialists and increase the efficiency of music creation. In the last five years, the quality of algorithm-based automatic music generation has increased significantly, motivated by the use of modern generative algorithms to learn the patterns implicit within a piece of music based on rule constraints or a musical corpus, thus generating music samples in various styles. Some of the available literature reviews lack a systematic benchmark of generative models and are traditional and conservative in their perspective, resulting in a vision of the future development of the field that is not deeply integrated with the current rapid scientific progress. In this paper, we conduct a comprehensive survey and analysis of recent intelligent music generation techniques,provide a critical discussion, explicitly identify their respective characteristics, and present them in a general table. We first introduce how music as a stream of information is encoded and the relevant datasets, then compare different types of generation algorithms, summarize their strengths and weaknesses, and discuss existing methods for evaluation. Finally, the development of artificial intelligence in composition is studied, especially by comparing the different characteristics of music generation techniques in the East and West and analyzing the development prospects in this field.
翻译:智能音乐是最受欢迎的计算机创造力的子领域之一,它能降低非专家的创造性门槛,提高音乐创作的效率。在过去5年中,以算法为基础的自动音乐制作的质量大大提高,其动机是利用现代基因化算法来学习基于规则限制或音乐内容的音乐中隐含的模式,从而产生不同风格的音乐样本。有些现有文献审查缺乏基因化模型的系统基准,从它们的角度来说是传统和保守的,导致对与当前快速科学进步没有深度结合的领域的未来发展前景。在本文件中,我们对最近的智能音乐制作技术进行了全面调查和分析,提供了批判性讨论,明确了它们各自的特点,并把它们放在一个总桌上。我们首先介绍了音乐作为信息流是如何编码和相关数据集的,然后比较了不同种类的生成算法,总结了它们的长处和弱点,并讨论了现有的评估方法。最后,通过比较东西方音乐制作技术的不同特点,并分析该领域的发展前景,研究了人造合成情报的发展。