Recent deep music generation studies have put much emphasis on long-term
generation with structures. However, we are yet to see high-quality,
well-structured whole-song generation. In this paper, we make the first attempt
to model a full music piece under the realization of compositional hierarchy.
With a focus on symbolic representations of pop songs, we define a hierarchical
language, in which each level of hierarchy focuses on the semantics and context
dependency at a certain music scope. The high-level languages reveal whole-song
form, phrase, and cadence, whereas the low-level languages focus on notes,
chords, and their local patterns. A cascaded diffusion model is trained to
model the hierarchical language, where each level is conditioned on its upper
levels. Experiments and analysis show that our model is capable of generating
full-piece music with recognizable global verse-chorus structure and cadences,
and the music quality is higher than the baselines. Additionally, we show that
the proposed model is controllable in a flexible way. By sampling from the
interpretable hierarchical languages or adjusting pre-trained external
representations, users can control the music flow via various features such as
phrase harmonic structures, rhythmic patterns, and accompaniment texture.

本文提出了基于组成层次结构实现完整音乐作品建模的方法，通过定义一种分层语言对流行歌曲的符号化表示进行建模。通过级联扩散模型训练了分层语言模型，实验证明该模型能够生成具有可识别的全局歌词 - 副歌结构和协调音的完整音乐作品，且音乐质量高于基准模型。此外，还展示了该模型具有灵活的可控性，用户可以通过从可解释的分层语言中采样或调整预训练的外部表示来控制音乐的流动。

基于级联扩散模型的符号音乐整曲层次生成

Whole-Song Hierarchical Generation of Symbolic Music Using Cascaded  Diffusion Models

This work proposes to combine neural networks with the compositional
hierarchy of human bodies for efficient and complete human parsing. We
formulate the approach as a neural information fusion framework. Our model
assembles the information from three inference processes over the hierarchy:
direct inference (directly predicting each part of a human body using image
information), bottom-up inference (assembling knowledge from constituent
parts), and top-down inference (leveraging context from parent nodes). The
bottom-up and top-down inferences explicitly model the compositional and
decompositional relations in human bodies, respectively. In addition, the
fusion of multi-source information is conditioned on the inputs, i.e., by
estimating and considering the confidence of the sources. The whole model is
end-to-end differentiable, explicitly modeling information flows and
structures. Our approach is extensively evaluated on four popular datasets,
outperforming the state-of-the-arts in all cases, with a fast processing speed
of 23fps. Our code and results have been released to help ease future research
in this direction.

该研究提出将神经网络与人体组成层次相结合，形成神经信息融合框架，以实现高效和完整的人体解析，并基于多源信息进行条件融合。在四个著名的数据集上进行了广泛的评估，并在处理速度方面具有快速性能，其代码和结果已经发布以便未来的研究。