Bird detection in audio : a survey and a challenge

Stowell, D; Wood, M; Stylianou, Y; Glotin, H

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.

Citation

Stowell, D., Wood, M., Stylianou, Y., & Glotin, H. (2016, September). Bird detection in audio : a survey and a challenge. Presented at 26th IEEE International Workshop on Machine Learning for Signal Processing, Salerno, Italy

• Presentation Conference Type: Other
• Conference Name: 26th IEEE International Workshop on Machine Learning for Signal Processing
• Conference Location: Salerno, Italy
• Start Date: Sep 13, 2016
• End Date: Sep 16, 2016
• Online Publication Date: Nov 10, 2016
• Publication Date: Nov 10, 2016
• Deposit Date: Jan 18, 2017
• Publicly Available Date: Jan 18, 2017
• Book Title: 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP) 13-16 Sept. 2016
• ISBN: 9781509007462
• DOI: https://doi.org/10.1109/MLSP.2016.7738875
• Publisher URL: http://dx.doi.org/10.1109/MLSP.2016.7738875
• Additional Information: Event Type : Conference; Funders : Natural Environment Research Council (NERC);Engineering and Physical Sciences Research Council (EPSRC); Projects : TREE;EP/L020505/1; Grant Number: NE/L000520/1