Decoupling Magnitude and Phase Estimation with Deep ResUNet for Music Source Separation

Understand

Deep neural network based methods have been successfully applied to music source separation.

• They typically learn a mapping from a mixture spectrogram to a set of source spectrograms, all with magnitudes only.
• This approach has several limitations: 1) its incorrect phase reconstruction degrades the performance, 2) it limits the magnitude of masks between 0 and 1 while we observe that 22% of time-frequency bins have ideal ratio mask values of over~1 in a popular dataset, MUSDB18, 3) its potential on very deep architectures is under-explored.
• Our proposed system is designed to overcome these.