Polyphonic training set synthesis improves self-supervised urban sound classification.

  title={Polyphonic training set synthesis improves self-supervised urban sound classification.},
  author={F{\'e}lix Gontier and Vincent Lostanlen and Mathieu Lagrange and Nicolas Fortin and Catherine Lavandier and Jean-François Petiot},
  journal={The Journal of the Acoustical Society of America},
  volume={149 6},
Machine listening systems for environmental acoustic monitoring face a shortage of expert annotations to be used as training data. To circumvent this issue, the emerging paradigm of self-supervised learning proposes to pre-train audio classifiers on a task whose ground truth is trivially available. Alternatively, training set synthesis consists in annotating a small corpus of acoustic events of interest, which are then automatically mixed at random to form a larger corpus of polyphonic scenes… 

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