  Audio tagging aims to assign one or several tags to an audio clip. Most of
the datasets are weakly labelled, which means only the tags of the clip are
known, without knowing the occurrence time of the tags. The labeling of an
audio clip is often based on the audio events in the clip and no event level
label is provided to the user. Previous works have used the bag of frames model
assume the tags occur all the time, which is not the case in practice. We
propose a joint detection-classification (JDC) model to detect and classify the
audio clip simultaneously. The JDC model has the ability to attend to
informative and ignore uninformative sounds. Then only informative regions are
used for classification. Experimental results on the "CHiME Home" dataset show
that the JDC model reduces the equal error rate (EER) from 19.0% to 16.9%. More
interestingly, the audio event detector is trained successfully without needing
the event level label.
