Label Tree Embeddings for Acoustic Scene Classification
  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. 
Huy Phan, Lars Hertel, Marco Maass, Philipp Koch, Alfred Mertins
cs.MM cs.AI cs.SD