Online Singing Voice Separation Using a Recurrent One-dimensional U-NET Trained with Deep Feature Losses
2019
This paper proposes an online approach to the singing voice separation problem. Based on a combination of one-dimensional convolutional layers along the frequency axis and recurrent layers to enforce temporal coherency, state-of-the-art performance is achieved. The concept of using deep features in the loss function to guide training and improve the model’s performance is also investigated.
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