  We describe in this report our audio scene recognition system submitted to
the DCASE 2016 challenge. Firstly, given the label set of the scenes, a label
tree is automatically constructed. This category taxonomy is then used in the
feature extraction step in which an audio scene instance is represented by a
label tree embedding image. Different convolutional neural networks, which are
tailored for the task at hand, are finally learned on top of the image features
for scene recognition. Our system reaches an overall recognition accuracy of
81.2% and 83.3% and outperforms the DCASE 2016 baseline with absolute
improvements of 8.7% and 6.1% on the development and test data, respectively.
