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.

Even though you're reading this article in English, there's a good chance it might not be your mother tongue. Of the billion-strong English speakers in the world, only 33% consider it their native language. The popularity of a language depends greatly on utility and geographic location. Additionally, how we measure the spread of world languages can vary greatly depending on whether you look at total speakers or native speakers. Today's detailed visualization from WordTips illustrates the 100 most spoken languages in the world, the number of native speakers for each language, and the origin tree that each language has branched out from.