Loza Mencía, Eneldo ; Nam, Jinseok ; Lee, Dong-Hyun
Hrsg.: Glotin, H. ; LeCun, Y. ; Mallat, Stéphane ; Tchernichovski, Ofer ; Artières, Thierry ; Halkias, Xanadu (2013)
Learning multi-labeled bioacoustic samples with an unsupervised feature learning approach.
Proceedings of Neural Information Scaled for Bioacoustics, from Neurons to Big Data.
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (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.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2013 |
| Herausgeber: | Glotin, H. ; LeCun, Y. ; Mallat, Stéphane ; Tchernichovski, Ofer ; Artières, Thierry ; Halkias, Xanadu |
| Autor(en): | Loza Mencía, Eneldo ; Nam, Jinseok ; Lee, Dong-Hyun |
| Art des Eintrags: | Bibliographie |
| Titel: | Learning multi-labeled bioacoustic samples with an unsupervised feature learning approach |
| Sprache: | Englisch |
| Publikationsjahr: | 2013 |
| Veranstaltungstitel: | Proceedings of Neural Information Scaled for Bioacoustics, from Neurons to Big Data |
| URL / URN: | http://www.ke.tu-darmstadt.de/publications/papers/lozanam201... |
| Kurzbeschreibung (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. |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Knowledge Engineering |
| Hinterlegungsdatum: | 25 Nov 2015 08:51 |
| Letzte Änderung: | 19 Dez 2018 14:36 |
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