Title: Weak Label Supervision for Monaural Source Separation Using Non-negative Denoising Variational Autoencoders,
Abstract: Deep learning models are very effective in source separation when there are
large amounts of labeled data available. However it is not always possible to
have carefully labeled datasets. In this paper, we propose a weak supervision
method that only uses class information rather than source signals for learning
to separate short utterance mixtures. We associate a variational autoencoder
(VAE) with each class within a non-negative model. We demonstrate that deep
convolutional VAEs provide a prior model to identify complex signals in a sound
mixture without having access to any source signal. We show that the separation
results are on par with source signal supervision.