ID  - lozanam2013nips4b
T1  - Learning multi-labeled bioacoustic samples with an unsupervised feature learning approach
A1  - Loza Mencía, Eneldo
A1  - Nam, Jinseok
A1  - Lee, Dong-Hyun
ED  - Glotin, H.
ED  - LeCun, Yann
ED  - Mallat, Stéphane
ED  - Tchernichovski, Ofer
ED  - Artières, Thierry
ED  - Halkias, Xanadu
TI  - Proceedings of Neural Information Scaled for Bioacoustics, from Neurons to Big Data
Y1  - 2013
SP  - 184
EP  - 189
T2  - NIPS Int. Conf.
SN  - 979-10-90821-04-0
N1  - Proceedings of NIPS4B workshop joint to NIPS
UR  - http://www.ke.tu-darmstadt.de/publications/papers/lozanam2013nips4b.pdf
N2  - 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.
ER  -