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Choral music separation refers to the task of extracting tracks of voice parts (e.g., soprano, alto, tenor, and bass) from mixed audio.
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H. Cuesta, E. G. Gutiérrez, A. M. Domínguez, and F. Loáiciga, “Analysis of intonation in unison choir singing,” in Proceedings of the International Conference of Music Perception and Cognition, ICMPC 2018
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F. Stöter, S. Uhlich, A. Liutkus, and Y. Mitsufuji, “Open-unmix - A reference implementation for music source separation,” J. Open Source Softw. , 2019
2019
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Q. Kong, Y. Cao, H. Liu, K. Choi, and Y. Wang, “Decoupling magnitude and phase estimation with deep resunet for music source separation,” in Proceedings of the 22nd International Society for Music Information Retrieval Conference, ISMIR 2021
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2020
Cited alongside, same era.
M. Gover and P. Depalle, “Score-informed source separation of choral music,” in Proceedings of the 21th International Society for Music Information Retrieval Conference, ISMIR 2020
2020
Cited alongside, same era.
D. Petermann, P. Chandna, H. Cuesta, J. Bonada, and E. Gómez, “Deep learning based source separation applied to choir ensembles,” in Proceedings of the 21th International Society for Music Information Retrieval Conference, ISMIR 2020
2020
Cited alongside, same era.
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2020
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K. Chen, C. Wang, T. Berg-Kirkpatrick, and S. Dubnov, “Music sketchnet: Controllable music generation via factorized representations of pitch and rhythm,” in Proceedings of the 21th International Society for Music Information Retrieval Conference, ISMIR 2020
2020
Cited alongside, same era.
2021
Later among the works it cites.
K. Chen, X. Du, B. Zhu, Z. Ma, T. Berg-Kirkpatrick, and S. Dubnov, “Zero-shot audio source separation through query-based learning from weakly-labeled data,” in AAAI Conference on Artificial Intelligence, AAAI 2022
2022
Closest in time.
H. Cuesta, “Data-driven pitch content description of choral singing recordings,” PhD Thesis Archive , 2022
2022
Closest in time.
Y. Wu and E. M. et al., “MIDI-DDSP: detailed control of musical performance via hierarchical modeling,” in the 10th International Conference on Learning Representations, ICLR 2022
2022
Closest in time.