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arXiv · 1606.07908

Label Tree Embeddings for Acoustic Scene Classification

Huy Phan·Lars Hertel·Marco Maass·Philipp Koch·Alfred Mertins
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Abstract

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.

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Huy Phan, Lars Hertel, Marco Maass, Philipp Koch, Alfred Mertins. 2016-06-25. Label Tree Embeddings for Acoustic Scene Classification. https://doi.org/10.1145/2964284.2967268

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