标题： MuseCoco：从文本生成符号音乐 显示英文标题

标题： MuseCoco: Generating Symbolic Music from Text

摘要： 从文本描述生成音乐是一种用户友好的模式，因为文本是用户参与相对容易的界面。 虽然一些方法利用文本来控制音乐音频的生成，但在生成的音频中编辑音乐元素对用户来说具有挑战性。 相比之下，符号音乐易于编辑，使用户更容易操作特定的音乐元素。 在本文中，我们提出了MuseCoco，它通过音乐属性作为桥梁，将任务分解为文本到属性理解和属性到音乐生成两个阶段，从而从文本描述生成符号音乐。 MuseCoCo代表音乐创作协作者，它使音乐家能够直接从给定的文本描述生成音乐，与完全从零开始创作音乐相比，效率有显著提高。 该系统有两个主要优势：首先，它数据高效。 在属性到音乐生成阶段，属性可以直接从音乐序列中提取，使模型训练自我监督。 在文本到属性理解阶段，文本是基于定义的属性模板由ChatGPT合成和优化的。 其次，该系统可以通过属性条件或文本条件的方法实现对文本描述中特定属性的精确控制，并提供多种控制选项。 MuseCoco在音乐性、可控性和总体评分方面分别至少优于基线系统1.27、1.08和1.32。 此外，客观控制准确率有约20%的显著提升。 此外，我们开发了一个具有12亿参数的鲁棒大规模模型，展示了卓越的可控性和音乐性。 显示英文摘要

摘要： Generating music from text descriptions is a user-friendly mode since the text is a relatively easy interface for user engagement. While some approaches utilize texts to control music audio generation, editing musical elements in generated audio is challenging for users. In contrast, symbolic music offers ease of editing, making it more accessible for users to manipulate specific musical elements. In this paper, we propose MuseCoco, which generates symbolic music from text descriptions with musical attributes as the bridge to break down the task into text-to-attribute understanding and attribute-to-music generation stages. MuseCoCo stands for Music Composition Copilot that empowers musicians to generate music directly from given text descriptions, offering a significant improvement in efficiency compared to creating music entirely from scratch. The system has two main advantages: Firstly, it is data efficient. In the attribute-to-music generation stage, the attributes can be directly extracted from music sequences, making the model training self-supervised. In the text-to-attribute understanding stage, the text is synthesized and refined by ChatGPT based on the defined attribute templates. Secondly, the system can achieve precise control with specific attributes in text descriptions and offers multiple control options through attribute-conditioned or text-conditioned approaches. MuseCoco outperforms baseline systems in terms of musicality, controllability, and overall score by at least 1.27, 1.08, and 1.32 respectively. Besides, there is a notable enhancement of about 20% in objective control accuracy. In addition, we have developed a robust large-scale model with 1.2 billion parameters, showcasing exceptional controllability and musicality.