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Found 4 papers, 1 papers with code
Why does music source separation benefit from cacophony?
In music source separation, a standard training data augmentation procedure is to create new training samples by randomly combining instrument stems from different songs.
Self-refining of Pseudo Labels for Music Source Separation with Noisy Labeled Data
Music source separation (MSS) faces challenges due to the limited availability of correctly-labeled individual instrument tracks.
MedleyVox: An Evaluation Dataset for Multiple Singing Voices Separation
Second, to overcome the absence of existing multi-singing datasets for a training purpose, we present a strategy for construction of multiple singing mixtures using various single-singing datasets.
Adversarially Trained End-to-end Korean Singing Voice Synthesis System
In this paper, we propose an end-to-end Korean singing voice synthesis system from lyrics and a symbolic melody using the following three novel approaches: 1) phonetic enhancement masking, 2) local conditioning of text and pitch to the super-resolution network, and 3) conditional adversarial training.
Sound Audio and Speech Processing
