Unsupervised Adversarial Domain Adaptation Based on The Wasserstein Distance For Acoustic Scene Classification

@article{Drossos2019UnsupervisedAD,
  title={Unsupervised Adversarial Domain Adaptation Based on The Wasserstein Distance For Acoustic Scene Classification},
  author={Konstantinos Drossos and Paul Magron and Tuomas Virtanen},
  journal={2019 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)},
  year={2019},
  pages={259-263}
}
  • K. Drossos, P. Magron, T. Virtanen
  • Published 24 April 2019
  • Computer Science
  • 2019 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)
A challenging problem in deep learning-based machine listening field is the degradation of the performance when using data from unseen conditions. In this paper we focus on the acoustic scene classification (ASC) task and propose an adversarial deep learning method to allow adapting an acoustic scene classification system to deal with a new acoustic channel resulting from data captured with a different recording device. We build upon the theoretical model of ℋΔℋ-distance and previous… 

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