  We present in this paper an efficient approach for acoustic scene
classification by exploring the structure of class labels. Given a set of class
labels, a category taxonomy is automatically learned by collectively optimizing
a clustering of the labels into multiple meta-classes in a tree structure. An
acoustic scene instance is then embedded into a low-dimensional feature
representation which consists of the likelihoods that it belongs to the
meta-classes. We demonstrate state-of-the-art results on two different datasets
for the acoustic scene classification task, including the DCASE 2013 and LITIS
Rouen datasets.
