Musical Metacreation tries to obtain creative behaviors from computers
algorithms composing music. In this paper I briefly analyze how this field
evolved from algorithmic composition to be focused on the search for
creativity, and I point out some issues in pursuing this goal. Finally, I argue
that hybridization of algorithms can be a useful direction for research.

本文简要分析了 Musical Metacreation 的演变过程，并指出了其中遇到的创新难题。最后，本文认为算法的混合可以是未来研究的有用方向。

音乐元创作中的挑战

Open Challenges in Musical Metacreation

While deep generative models have become the leading methods for algorithmic
composition, it remains a challenging problem to control the generation process
because the latent variables of most deep-learning models lack good
interpretability. Inspired by the content-style disentanglement idea, we design
a novel architecture, under the VAE framework, that effectively learns two
interpretable latent factors of polyphonic music: chord and texture. The
current model focuses on learning 8-beat long piano composition segments. We
show that such chord-texture disentanglement provides a controllable generation
pathway leading to a wide spectrum of applications, including compositional
style transfer, texture variation, and accompaniment arrangement. Both
objective and subjective evaluations show that our method achieves a successful
disentanglement and high quality controlled music generation.

本文提出了一种基于 VAE 的音乐分离模型，用于将和弦和纹理这两个可解释的因子分离出来，从而实现音乐生成过程的可控，在生成音乐时具有广泛的应用，包括作曲风格转换、纹理变化和伴奏编排，并通过客观和主观评价证明了该方法的成功分离和高质量的音乐生成。

学习可解释的控制式复调音乐生成的表示

Learning Interpretable Representation for Controllable Polyphonic Music  Generation

Algorithmic composition is the partial or total automation of the process of
music composition by using computers. Since the 1950s, different computational
techniques related to Artificial Intelligence have been used for algorithmic
composition, including grammatical representations, probabilistic methods,
neural networks, symbolic rule-based systems, constraint programming and
evolutionary algorithms. This survey aims to be a comprehensive account of
research on algorithmic composition, presenting a thorough view of the field
for researchers in Artificial Intelligence.

本文综述算法作曲的研究进展，介绍了在人工智能领域中与符号规则系统、神经网络、演化算法等相关的计算技术，旨在为人工智能研究人员提供全面的领域视角。