  Audio event classification is an active research area and has a wide range of
applications. Since the release of AudioSet, great progress has been made in
advancing the classification accuracy, which mostly comes from the development
of novel model architectures and attention modules. However, we find that
appropriate training techniques are equally important for building audio event
classification models with AudioSet, but have not received the attention they
deserve. To fill the gap, in this work, we present PSLA, a collection of
training techniques that can noticeably boost the model accuracy including
ImageNet pretraining, balanced sampling, data augmentation, label enhancement,
model aggregation and their design choices. By training an EfficientNet with
these techniques, we obtain a model that achieves a new state-of-the-art mean
average precision (mAP) of 0.474 on AudioSet, outperforming the previous best
system of 0.439.
