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A natural question arising in Music Source Separation (MSS) is whether long range contextual information is useful, or whether local acoustic features are sufficient.
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Diederik P. Kingma and Jimmy Ba, · 2014
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“The 2015 Signal Separation Evaluation Campaign,”
Nobutaka Ono, Zafar Rafii, Daichi Kitamura, Nobutaka Ito, and Antoine Liutkus, · 2015
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Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin, · 2017
Earlier work this paper cites.
“The musdb18 corpus for music separation,” 2017
Zafar Rafii, Antoine Liutkus, Fabian-Robert Stöter, Stylianos Ioannis Mimilakis, and Rachel Bittner, · 2017
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“Wave-u-net: A multi-scale neural network for end-to-end audio source separation,”
Daniel Stoller, Sebastian Ewert, and Simon Dixon, · 2018
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“The 2018 signal separation evaluation campaign,” 2018
Fabian-Robert Stöter, Antoine Liutkus, and Nobutaka Ito, · 2018
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“Musdb18-hq - an uncompressed version of musdb18,” Aug. 2019
Zafar Rafii, Antoine Liutkus, Fabian-Robert Stöter, Stylianos Ioannis Mimilakis, and Rachel Bittner, · 2019
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“Conv-tasnet: Surpassing ideal time–frequency magnitude masking for speech separation,”
Yi Luo and Nima Mesgarani, · 2019
Earlier work this paper cites.
“Music source separation in the waveform domain,” 2019
Alexandre Défossez, Nicolas Usunier, Léon Bottou, and Francis Bach, · 2019
Cited alongside, same era.
“Open-unmix - a reference implementation for music source separation,”
F.-R. Stöter, S. Uhlich, A. Liutkus, and Y. Mitsufuji, · 2019
Cited alongside, same era.
“Translating math formula images to latex sequences using deep neural networks with sequence-level training,” 2019
Zelun Wang and Jyh-Charn Liu, · 2019
Cited alongside, same era.
“Language models are few-shot learners,”
Tom B. Brown et al., · 2020
Cited alongside, same era.
“D3net: Densely connected multidilated densenet for music source separation,” 2020
Naoya Takahashi and Yuki Mitsufuji, · 2020
Cited alongside, same era.
“Dual-path rnn: efficient long sequence modeling for time-domain single-channel speech separation,”
“Going deeper with image transformers,”
Hugo Touvron, Matthieu Cord, Alexandre Sablayrolles, Gabriel Synnaeve, and Hervé Jégou, · 2021
Later among the works it cites.
“Lasaft: Latent source attentive frequency transformation for conditioned source separation,”
Woosung Choi, Minseok Kim, Jaehwa Chung, and Soonyoung Jung, · 2021
Later among the works it cites.
“Kuielab-mdx-net: A two-stream neural network for music demixing,” 2021
Minseok Kim, Woosung Choi, Jaehwa Chung, Daewon Lee, and Soonyoung Jung, · 2021
Later among the works it cites.
“Attention is all you need in speech separation,”
Cem Subakan, Mirco Ravanelli, Samuele Cornell, Mirko Bronzi, and Jianyuan Zhong, · 2021
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“xformers: A modular and hackable transformer modelling library,” https://github.com/facebookresearch/xformers , 2021
Benjamin Lefaudeux, Francisco Massa, Diana Liskovich, Wenhan Xiong, Vittorio Caggiano, Sean Naren, Min Xu, Jieru Hu, Marta Tintore, and Susan Zhang, · 2021
Later among the works it cites.
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Yi Luo, Zhuo Chen, and Takuya Yoshioka, · 2020
Cited alongside, same era.
“Spleeter: a fast and efficient music source separation tool with pre-trained models,”
Romain Hennequin, Anis Khlif, Felix Voituret, and Manuel Moussallam, · 2020
Cited alongside, same era.
“Hybrid spectrogram and waveform source separation,”
Alexandre Défossez, · 2021
Cited alongside, same era.
“High-resolution image synthesis with latent diffusion models,”
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer, · 2022
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“Music source separation with band-split rnn,” 2022
Yi Luo and Jianwei Yu, · 2022
Closest in time.
“Music demixing challenge 2021,”
Yuki Mitsufuji, Giorgio Fabbro, Stefan Uhlich, Fabian-Robert Stöter, Alexandre Défossez, Minseok Kim, Woosung Choi, Chin-Yun Yu, and Kin-Wai Cheuk, · 2022
Closest in time.