# Remastering Divide and Remaster: A Cinematic Audio Source Separation Dataset with Multilingual Support

arXiv: https://arxiv.org/abs/2407.07275 · HTML: https://mlsanity.com/paper/2407.07275/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 51 references that are in the map.
- First posted on arXiv 9 Jul 2024.

## Builds on (most cited first, 11 of 11)

- [FSD50K: An Open Dataset of Human-Labeled Sound Events](https://mlsanity.com/paper/2010.00475/) (2020)
- [FMA: A Dataset For Music Analysis](https://mlsanity.com/paper/1612.01840/) (2016)
- [Universal Sound Separation](https://mlsanity.com/paper/1905.03330/) (2019)
- [The Cocktail Fork Problem: Three-Stem Audio Separation for Real-World Soundtracks](https://mlsanity.com/paper/2110.09958/) (2021)
- [HUI-Audio-Corpus-German: A high quality TTS dataset](https://mlsanity.com/paper/2106.06309/) (2021)
- [The Sound Demixing Challenge 2023 unicodex2013 Cinematic Demixing Track](https://mlsanity.com/paper/2308.06981/) (2023)
- [JVNV: A Corpus of Japanese Emotional Speech with Verbal Content and Nonverbal Expressions](https://mlsanity.com/paper/2310.06072/) (2023)
- [A Generalized Bandsplit Neural Network for Cinematic Audio Source Separation](https://mlsanity.com/paper/2309.02539/) (2023)
- [PJS: phoneme-balanced Japanese singing voice corpus](https://mlsanity.com/paper/2006.02959/) (2020)
- [Google Crowdsourced Speech Corpora and Related Open-Source Resources for Low-Resource Languages and Dialects: An Overview](https://mlsanity.com/paper/2010.06778/) (2020)
- [Tackling the Cocktail Fork Problem for Separation and Transcription of Real-World Soundtracks](https://mlsanity.com/paper/2212.07327/) (2022)

## Built on by (most cited first, 5 of 5)

- [Learning to Highlight Audio by Watching Movies](https://mlsanity.com/paper/2505.12154/) (2025)
- [Semantic visually-guided acoustic highlighting with large vision-language models](https://mlsanity.com/paper/2601.08871/) (2026)
- [Facing the Music: Tackling Singing Voice Separation in Cinematic Audio Source Separation](https://mlsanity.com/paper/2408.03588/) (2024)
- [A Knowledge-Driven Approach to Music Segmentation, Music Source Separation and Cinematic Audio Source Separation](https://mlsanity.com/paper/2602.21476/) (2026)
- [Spot, Separate, and Enhance: Fully Generative Approach for Audio Mixing](https://mlsanity.com/paper/2609.29169/) (2026)

## Abstract (arXiv)

Cinematic audio source separation (CASS), as a problem of extracting the dialogue, music, and effects stems from their mixture, is a relatively new subtask of audio source separation. 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. Dataset and model implementation will be made available at https://github.com/kwatcharasupat/source-separation-landing.