Corpus ID: 165163960

CASS: Cross Adversarial Source Separation via Autoencoder

@article{Ong2019CASSCA,
  title={CASS: Cross Adversarial Source Separation via Autoencoder},
  author={Yong Zheng Ong and Charles K. Chui and Haizhao Yang},
  journal={ArXiv},
  year={2019},
  volume={abs/1905.09877}
}
This paper introduces a cross adversarial source separation (CASS) framework via autoencoder, a new model that aims at separating an input signal consisting of a mixture of multiple components into individual components defined via adversarial learning and autoencoder fitting. CASS unifies popular generative networks like auto-encoders (AEs) and generative adversarial networks (GANs) in a single framework. The basic building block that filters the input signal and reconstructs the $i$-th target… Expand
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