  Audio tagging aims to perform multi-label classification on audio chunks and
it is a newly proposed task in the Detection and Classification of Acoustic
Scenes and Events 2016 (DCASE 2016) challenge. This task encourages research
efforts to better analyze and understand the content of the huge amounts of
audio data on the web. The difficulty in audio tagging is that it only has a
chunk-level label without a frame-level label. This paper presents a weakly
supervised method to not only predict the tags but also indicate the temporal
locations of the occurred acoustic events. The attention scheme is found to be
effective in identifying the important frames while ignoring the unrelated
frames. The proposed framework is a deep convolutional recurrent model with two
auxiliary modules: an attention module and a localization module. The proposed
algorithm was evaluated on the Task 4 of DCASE 2016 challenge. State-of-the-art
performance was achieved on the evaluation set with equal error rate (EER)
reduced from 0.13 to 0.11, compared with the convolutional recurrent baseline
system.
