  State-of-the-art under-determined audio source separation systems rely on
supervised end-end training of carefully tailored neural network architectures
operating either in the time or the spectral domain. However, these methods are
severely challenged in terms of requiring access to expensive source level
labeled data and being specific to a given set of sources and the mixing
process, which demands complete re-training when those assumptions change. This
strongly emphasizes the need for unsupervised methods that can leverage the
recent advances in data-driven modeling, and compensate for the lack of labeled
data through meaningful priors. To this end, we propose a novel approach for
audio source separation based on generative priors trained on individual
sources. Through the use of projected gradient descent optimization, our
approach simultaneously searches in the source-specific latent spaces to
effectively recover the constituent sources. Though the generative priors can
be defined in the time domain directly, e.g. WaveGAN, we find that using
spectral domain loss functions for our optimization leads to good-quality
source estimates. Our empirical studies on standard spoken digit and instrument
datasets clearly demonstrate the effectiveness of our approach over classical
as well as state-of-the-art unsupervised baselines.
