Addressing Missing Labels in Large-Scale Sound Event Recognition Using a Teacher-Student Framework With Loss Masking

@article{Fonseca2020AddressingML,
  title={Addressing Missing Labels in Large-Scale Sound Event Recognition Using a Teacher-Student Framework With Loss Masking},
  author={Eduardo Fonseca and Shawn Hershey and Manoj Plakal and Daniel P. W. Ellis and Aren Jansen and R. Channing Moore},
  journal={IEEE Signal Processing Letters},
  year={2020},
  volume={27},
  pages={1235-1239}
}
The study of label noise in sound event recognition has recently gained attention with the advent of larger and noisier datasets. This work addresses the problem of missing labels, one of the big weaknesses of large audio datasets, and one of the most conspicuous issues for AudioSet. We propose a simple and model-agnostic method based on a teacher-student framework with loss masking to first identify the most critical missing label candidates, and then ignore their contribution during the… 
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