[BibTeX] [RIS]
Learning multi-labeled bioacoustic samples with an unsupervised feature learning approach
Type of publication: Inproceedings
Citation: lozanam2013nips4b
Booktitle: Proceedings of Neural Information Scaled for Bioacoustics, from Neurons to Big Data
Year: 2013
Month: December
Pages: 184-189
Organization: NIPS Int. Conf.
Note: Proceedings of NIPS4B workshop joint to NIPS
ISSN: 979-10-90821-04-0
URL: http://www.ke.tu-darmstadt.de/publications/papers/lozanam2013nips4b.pdf
Abstract: Multi-label Bird Species Classification competition provides an excellent oppor- tunity to analyze the effectiveness of acoustic processing and mutlilabel learning. We propose an unsupervised feature extraction and generation approach based on latest advances in deep neural network learning, which can be applied generically to acoustic data. With state-of-the-art approaches from multilabel learning, we achieved top positions in the competition, only surpassed by teams with profound expertise in acoustic data processing.
Keywords:
Authors Loza Mencía, Eneldo
Nam, Jinseok
Lee, Dong-Hyun
Editors Glotin, H.
LeCun, Y.
Mallat, Stéphane
Tchernichovski, Ofer
Artières, Thierry
Halkias, Xanadu
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