TY - CONF 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 - /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 -