  Acoustic event detection for content analysis in most cases relies on lots of
labeled data. However, manually annotating data is a time-consuming task, which
thus makes few annotated resources available so far. Unlike audio event
detection, automatic audio tagging, a multi-label acoustic event classification
task, only relies on weakly labeled data. This is highly desirable to some
practical applications using audio analysis. In this paper we propose to use a
fully deep neural network (DNN) framework to handle the multi-label
classification task in a regression way. Considering that only chunk-level
rather than frame-level labels are available, the whole or almost whole frames
of the chunk were fed into the DNN to perform a multi-label regression for the
expected tags. The fully DNN, which is regarded as an encoding function, can
well map the audio features sequence to a multi-tag vector. A deep pyramid
structure was also designed to extract more robust high-level features related
to the target tags. Further improved methods were adopted, such as the Dropout
and background noise aware training, to enhance its generalization capability
for new audio recordings in mismatched environments. Compared with the
conventional Gaussian Mixture Model (GMM) and support vector machine (SVM)
methods, the proposed fully DNN-based method could well utilize the long-term
temporal information with the whole chunk as the input. The results show that
our approach obtained a 15% relative improvement compared with the official
GMM-based method of DCASE 2016 challenge.
