Improving singing voice separation with the Wave-U-Net using Minimum Hyperspherical Energy

Understand

In recent years, deep learning has surpassed traditional approaches to the problem of singing voice separation.

• The Wave-U-Net is a recent deep network architecture that operates directly on the time domain.
• The standard Wave-U-Net is trained with data augmentation and early stopping to prevent overfitting.
• Minimum hyperspherical energy (MHE) regularization has recently proven to increase generalization in image classification problems by encouraging a diversified filter configuration.