  Environmental audio tagging aims to predict only the presence or absence of
certain acoustic events in the interested acoustic scene. In this paper we make
contributions to audio tagging in two parts, respectively, acoustic modeling
and feature learning. We propose to use a shrinking deep neural network (DNN)
framework incorporating unsupervised feature learning to handle the multi-label
classification task. For the acoustic modeling, a large set of contextual
frames of the chunk are fed into the DNN to perform a multi-label
classification for the expected tags, considering that only chunk (or
utterance) level rather than frame-level labels are available. Dropout and
background noise aware training are also adopted to improve the generalization
capability of the DNNs. For the unsupervised feature learning, we propose to
use a symmetric or asymmetric deep de-noising auto-encoder (sDAE or aDAE) to
generate new data-driven features from the Mel-Filter Banks (MFBs) features.
The new features, which are smoothed against background noise and more compact
with contextual information, can further improve the performance of the DNN
baseline. Compared with the standard Gaussian Mixture Model (GMM) baseline of
the DCASE 2016 audio tagging challenge, our proposed method obtains a
significant equal error rate (EER) reduction from 0.21 to 0.13 on the
development set. The proposed aDAE system can get a relative 6.7% EER reduction
compared with the strong DNN baseline on the development set. Finally, the
results also show that our approach obtains the state-of-the-art performance
with 0.15 EER on the evaluation set of the DCASE 2016 audio tagging task while
EER of the first prize of this challenge is 0.17.
