  After its sweeping success in vision and language tasks, pure attention-based
neural architectures (e.g. DeiT) are emerging to the top of audio tagging (AT)
leaderboards, which seemingly obsoletes traditional convolutional neural
networks (CNNs), feed-forward networks or recurrent networks. However, taking a
closer look, there is great variability in published research, for instance,
performances of models initialized with pretrained weights differ drastically
from without pretraining, training time for a model varies from hours to weeks,
and often, essences are hidden in seemingly trivial details. This urgently
calls for a comprehensive study since our 1st comparison is half-decade old. In
this work, we perform extensive experiments on AudioSet which is the largest
weakly-labeled sound event dataset available, we also did an analysis based on
the data quality and efficiency. We compare a few state-of-the-art baselines on
the AT task, and study the performance and efficiency of 2 major categories of
neural architectures: CNN variants and attention-based variants. We also
closely examine their optimization procedures. Our opensourced experimental
results provide insights to trade-off between performance, efficiency,
optimization process, for both practitioners and researchers. Implementation:
https://github.com/lijuncheng16/AudioTaggingDoneRight
