情感音乐生成中消除主观偏见的EmoGen

EmoGen: Eliminating Subjective Bias in Emotional Music Generation

July 3, 2023

作者: Chenfei Kang, Peiling Lu, Botao Yu, Xu Tan, Wei Ye, Shikun Zhang, Jiang Bian

cs.AI

摘要

音乐被用来传达情感，因此在自动音乐生成中生成情感音乐是很重要的。先前关于情感音乐生成的研究直接使用带有情感标签的注释作为控制信号，这种方法存在主观偏见：不同的人可能会在同一首音乐上注释不同的情感，一个人在不同情境下可能会感受到不同的情感。因此，直接将情感标签与音乐序列以端到端的方式进行映射会混淆学习过程，并阻碍模型生成具有普遍情感的音乐。在本文中，我们提出了EmoGen，一种情感音乐生成系统，它利用一组与情感相关的音乐属性作为情感和音乐之间的桥梁，并将生成分为两个阶段：通过受监督的聚类进行情感到属性的映射，以及通过自监督学习进行属性到音乐的生成。这两个阶段都是有益的：在第一个阶段，围绕聚类中心的属性值代表这些样本的普遍情感，有助于消除情感标签的主观偏见的影响；在第二阶段，生成完全与情感标签解耦，因此不受主观偏见的影响。主观和客观评估都表明EmoGen在情感控制准确性和音乐质量方面均优于先前的方法，这证明了我们在生成情感音乐方面的优越性。EmoGen生成的音乐样本可通过此链接获得：https://ai-muzic.github.io/emogen/，代码可通过此链接获得：https://github.com/microsoft/muzic/。

English

Music is used to convey emotions, and thus generating emotional music is important in automatic music generation. Previous work on emotional music generation directly uses annotated emotion labels as control signals, which suffers from subjective bias: different people may annotate different emotions on the same music, and one person may feel different emotions under different situations. Therefore, directly mapping emotion labels to music sequences in an end-to-end way would confuse the learning process and hinder the model from generating music with general emotions. In this paper, we propose EmoGen, an emotional music generation system that leverages a set of emotion-related music attributes as the bridge between emotion and music, and divides the generation into two stages: emotion-to-attribute mapping with supervised clustering, and attribute-to-music generation with self-supervised learning. Both stages are beneficial: in the first stage, the attribute values around the clustering center represent the general emotions of these samples, which help eliminate the impacts of the subjective bias of emotion labels; in the second stage, the generation is completely disentangled from emotion labels and thus free from the subjective bias. Both subjective and objective evaluations show that EmoGen outperforms previous methods on emotion control accuracy and music quality respectively, which demonstrate our superiority in generating emotional music. Music samples generated by EmoGen are available via this link:https://ai-muzic.github.io/emogen/, and the code is available at this link:https://github.com/microsoft/muzic/.