论文标题
一项关于深音乐的综合调查：多层表示，算法，评估和未来的方向
A Comprehensive Survey on Deep Music Generation: Multi-level Representations, Algorithms, Evaluations, and Future Directions
论文作者
论文摘要
深度学习技术在生成各种内容（例如图像，文本等）中的利用已成为一种趋势。尤其是本文的主题，音乐引起了无数研究人员的广泛关注。制作音乐的整个过程可以分为三个阶段，对应于三个级别的音乐发电：得分生成产生分数，表演生成为分数增添了性能特征，并通过将音频特征分配给音调或生成音频形式的音频，使得分数转换为音频特征或直接将音乐特征分配到音频中。先前的调查探索了自动音乐生成领域中使用的网络模型。但是，尚未清楚地说明发展历史，模型演变以及同一音乐发电任务的利弊。本文试图在不同的音乐生成水平下概述各种作曲任务，涵盖了使用深度学习的大多数流行音乐生成任务。此外，我们总结了适合各种任务的数据集，讨论音乐表示形式，评估方法以及在不同级别下的挑战，最后指出了几个未来的方向。
The utilization of deep learning techniques in generating various contents (such as image, text, etc.) has become a trend. Especially music, the topic of this paper, has attracted widespread attention of countless researchers.The whole process of producing music can be divided into three stages, corresponding to the three levels of music generation: score generation produces scores, performance generation adds performance characteristics to the scores, and audio generation converts scores with performance characteristics into audio by assigning timbre or generates music in audio format directly. Previous surveys have explored the network models employed in the field of automatic music generation. However, the development history, the model evolution, as well as the pros and cons of same music generation task have not been clearly illustrated. This paper attempts to provide an overview of various composition tasks under different music generation levels, covering most of the currently popular music generation tasks using deep learning. In addition, we summarize the datasets suitable for diverse tasks, discuss the music representations, the evaluation methods as well as the challenges under different levels, and finally point out several future directions.
