Name
Abstract | ||
|---|---|---|
Many biological monitoring projects rely on acoustic detection of birds. Despite increasingly large datasets, this detection is often manual or semi-automatic, requiring manual tuning/postprocessing. We review the state of the art in automatic bird sound detection, and identify a widespread need for tuning-free and species-agnostic approaches. We introduce new datasets and an IEEE research challenge to address this need, to make possible the development of fully automatic algorithms for bird sound detection. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/MLSP.2016.7738875 | 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP) |
Keywords | DocType | Volume |
audio,biological monitoring projects,bird acoustic detection,automatic bird sound detection,tuning-free approaches,species-agnostic approaches | Conference | abs/1608.03417 |
ISSN | ISBN | Citations |
2161-0363 | 978-1-5090-0747-9 | 17 |
PageRank | References | Authors |
1.20 | 12 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dan Stowell | 1 | 209 | 21.84 |
| Mike Wood | 2 | 17 | 1.20 |
| Yannis Stylianou | 3 | 1436 | 140.45 |
| Hervé Glotin | 4 | 309 | 45.05 |