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Introduction:
\n\nDivide and Remaster (DnR) is a source separation dataset for training and testing algorithms that separate a monaural audio signal into speech, music, and sound effects/background stems. The dataset is composed of artificial mixtures using audio from the librispeech, free music archive (FMA), and Freesound Dataset 50k (FSD50k). We introduce it as part of the Cocktail Fork Problem paper.
\n\n\n\n
At a Glance:
\n\n.wav
files at a sampling rate of 44.1 kHz
tr
(3295 mixtues), validation cv
(440 mixtures) and testing tt
(652 mixtures) subsets.wav
files, mix.wav
, music.wav
, speech.wav
, sfx.wav
, and annots.csv
which contains the metadata for the original audio used to compose the mixture (transcriptions for speech, sound classes for sfx, and genre labels for music)\n\n
Other Resources:
\n\nDemo examples and additional information are available at: https://cocktail-fork.github.io/
\n\nFor more details about the data generation process, the code used to generate our dataset can be found at the following: https://github.com/darius522/dnr-utils
\n\n\n\n
Contact and Support:
\n\nHave an issue, concern, or question about DnR ? If so, please open an issue here.
\n\nFor any other inquiries, feel free to shoot an email at: firstname.lastname@gmail.com, my name is Darius Petermann ;)
\n\n\n\n
Citation:
\n\nIf you use DnR please cite [our paper](https://arxiv.org/abs/2110.09958) in which we introduce the dataset as part of the Cocktail Fork Problem:
\n\n@article{Petermann2021cocktail,\n\u00a0 \u00a0 title={The Cocktail Fork Problem: Three-Stem Audio Separation for Real-World Soundtracks},\u00a0\n\u00a0 \u00a0 author={Darius Petermann and Gordon Wichern and Zhong-Qiu Wang and Jonathan {Le Roux}},\n\u00a0 \u00a0 year={2021},\n\u00a0 \u00a0 journal={arXiv preprint arXiv:2110.09958},\n\u00a0 \u00a0 archivePrefix={arXiv},\n\u00a0 \u00a0 primaryClass={eess.AS}\n}
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Mitsubishi Electric Research Laboratories (MERL) is the US subsidiary of the corporate research and development organization of Mitsubishi Electric Corporation. MERL conducts application-motivated basic research and advanced development in: Physical Modeling & Simulation, Signal Processing, Control, Optimization, and Artificial Intelligence.
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