Cinematic audio source separation (CASS) is a relatively new subtask of audio source separation, concerned with the separation of a mixture into the dialogue, music, and effects stems. To date, only one publicly available dataset exists for CASS, that is, the Divide and Remaster (DnR) dataset, which is currently at version 2. While DnR v2 has been an incredibly useful resource for CASS, several areas of improvement have been identified, particularly through its use in the 2023 Sound Demixing Challenge. In this work, we develop version 3 of the DnR dataset, addressing issues relating to vocal content in non-dialogue stems, loudness distributions, mastering process, and linguistic diversity. In particular, the dialogue stem of DnR v3 includes speech content from more than 30 languages from multiple families including but not limited to the Germanic, Romance, Indo-Aryan, Dravidian, Malayo-Polynesian, and Bantu families. Benchmark results using the Bandit model indicated that training on multilingual data yields significant generalizability to the model even in languages with low data availability. Even in languages with high data availability, the multilingual model often performs on par or better than dedicated models trained on monolingual CASS datasets.

开发了DnR数据集的版本3，处理了与非对话声音站点中的语音内容、音量分布、母带处理和语言多样性相关的问题。通过Bandit模型的基准结果表明，使用多语言数据进行训练在模型中产生了显著的一般化能力，即使在数据可用性较低的语言中也如此。即使在数据可用性较高的语言中，多语言模型通常表现得与在单语种CASS数据集上训练的专用模型相当或更好。

重新制作《分割与重新制作》：一个具备多语言支持的电影音频源分离数据集