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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.doi:10.1121/10.0005277 pmid:34241459 fatcat:55r5axrot5gfhngrthmopnlcxm