Audio tagging is the task of predicting the presence or absence of sound
classes within an audio clip. Previous work in audio tagging focused on
relatively small datasets limited to recognising a small number of sound
classes. We investigate audio tagging on AudioSet, which is a dataset
consisting of over 2 million audio clips and 527 classes. AudioSet is weakly
labelled, in that only the presence or absence of sound classes is known for
each clip, while the onset and offset times are unknown. To address the
weakly-labelled audio tagging problem, we propose attention neural networks as
a way to attend the most salient parts of an audio clip. We bridge the
connection between attention neural networks and multiple instance learning
(MIL) methods, and propose decision-level and feature-level attention neural
networks for audio tagging. We investigate attention neural networks modeled by
different functions, depths and widths. Experiments on AudioSet show that the
feature-level attention neural network achieves a state-of-the-art mean average
precision (mAP) of 0.369, outperforming the best multiple instance learning
(MIL) method of 0.317 and Google's deep neural network baseline of 0.314. In
addition, we discover that the audio tagging performance on AudioSet embedding
features has a weak correlation with the number of training samples and the
quality of labels of each sound class.Comment: 13 page