Learning Contextual Tag Embeddings for Cross-Modal Alignment of Audio and Tags

@article{Favory2021LearningCT,
  title={Learning Contextual Tag Embeddings for Cross-Modal Alignment of Audio and Tags},
  author={Xavier Favory and Konstantinos Drossos and Tuomas Virtanen and Xavier Serra},
  journal={ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  year={2021},
  pages={596-600}
}
  • Xavier Favory, K. Drossos, X. Serra
  • Published 27 October 2020
  • Computer Science
  • ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Self-supervised audio representation learning offers an attractive alternative for obtaining generic audio embeddings, capable to be employed into various downstream tasks. Published approaches that consider both audio and words/tags associated with audio do not employ text processing models that are capable to generalize to tags unknown during training. In this work we propose a method for learning audio representations using an audio autoencoder (AAE), a general word embed-dings model (WEM… 
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