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.
Original language | English |
---|---|
Title of host publication | Proceedings of the 2016 ACM on Multimedia Conference |
Number of pages | 5 |
Place of Publication | New York, NY, USA |
Publisher | ACM |
Publication date | 01.10.2016 |
Pages | 486-490 |
ISBN (Print) | 978-1-4503-3603-1 |
DOIs | |
Publication status | Published - 01.10.2016 |
Event | 24th ACM Multimedia Conference - Amsterdam, Netherlands Duration: 15.10.2016 → 19.10.2016 Conference number: 124107 |