  Recent progress in network-based audio event classification has shown the
benefit of pre-training models on visual data such as ImageNet. While this
process allows knowledge transfer across different domains, training a model on
large-scale visual datasets is time consuming. On several audio event
classification benchmarks, we show a fast and effective alternative that
pre-trains the model unsupervised, only on audio data and yet delivers on-par
performance with ImageNet pre-training. Furthermore, we show that our
discriminative audio learning can be used to transfer knowledge across audio
datasets and optionally include ImageNet pre-training.
