Distribution Preserving Source Separation With Time Frequency Predictive Models
This work addresses perceptual issues in audio source separation, which is important for applications like music production and hearing aids, but appears incremental as it builds on existing generative models.
The paper tackled the problem of source separation by introducing a distribution preserving method to address perceptual shortcomings in state-of-the-art approaches, achieving improved results in listening tests.
We provide an example of a distribution preserving source separation method, which aims at addressing perceptual shortcomings of state-of-the-art methods. Our approach uses unconditioned generative models of signal sources. Reconstruction is achieved by means of mix-consistent sampling from a distribution conditioned on a realization of a mix. The separated signals follow their respective source distributions, which provides an advantage when separation results are evaluated in a listening test.