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Singing Voice Separation: A Study on Training Data

Abstract : In the recent years, singing voice separation systems showed increased performance due to the use of supervised training. The design of training datasets is known as a crucial factor in the performance of such systems. We investigate on how the characteristics of the training dataset impacts the separation performances of state-of-the-art singing voice separation algorithms. We show that the separation quality and diversity are two important and complementary assets of a good training dataset. We also provide insights on possible transforms to perform data augmentation for this task.
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Submitted on : Wednesday, November 20, 2019 - 11:20:13 AM
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Laure Prétet, Romain Hennequin, Jimena Royo-Letelier, Andrea Vaglio. Singing Voice Separation: A Study on Training Data. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 2019, Brighton, United Kingdom. pp.506-510, ⟨10.1109/ICASSP.2019.8683555⟩. ⟨hal-02372076⟩



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