@article {57619, title = {NIPS4Bplus: a richly annotated birdsong audio dataset}, year = {2019}, abstract = {

Recent advances in birdsong detection and classification have approached a limit due to the lack of fully annotated recordings. In this paper, we present NIPS4Bplus, the first richly annotated birdsong audio dataset, that is comprised of recordings containing bird vocalisations along with their active species tags plus the temporal annotations acquired for them. Statistical information about the recordings, their species specific tags and their temporal annotations are presented along with example uses. NIPS4Bplus could be used in various ecoacoustic tasks, such as training models for bird population monitoring, species classification, birdsong vocalisation detection and classification.

}, keywords = {audio dataset, Audio signal processing, bioacoustics, Bioinformatics, Bird vocalisations, ecoacoustics, ecosystems, rich annotations}, doi = {10.7717/peerj-cs.223}, url = {https://peerj.com/articles/cs-223}, author = {Morfi, Veronica and Bas, Yves and Pamu{\l}a, Hanna and Glotin, Herv{\'e} and Stowell, Dan} } @article {53274, title = {NIPS4Bplus: a richly annotated birdsong audio dataset}, journal = {arXiv preprint arXiv:1811.02275}, year = {2018}, abstract = {

Recent advances in birdsong detection and classification have approached a limit due to the lack of fully annotated record- ings. In this paper, we present NIPS4Bplus, the first richly annotated birdsong audio dataset, that is comprised of record- ings containing bird vocalisations along with their active species tags plus the temporal annotations acquired for them. Statistical information about the recordings, their species spe- cific tags and their temporal annotations are presented along with example uses. NIPS4Bplus could be used in various ecoacoustic tasks, such as training models for bird popula- tion monitoring, species classification, birdsong vocalisation detection and classification.

}, keywords = {audio dataset, bird vocalisa- tions, ecoacoustics, ecosystems, rich annotations}, author = {Morfi, Veronica and Bas, Yves and Pamu{\l}a, Hanna and Glotin, Herv{\'e} and Stowell, Dan} } @inbook {50907, title = {Computational Analysis of Sound Scenes and Events}, booktitle = {Computational Analysis of Sound Scenes and Events}, year = {2018}, pages = {303 - 333}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, chapter = {11}, address = {Cham}, isbn = {978-3-319-63449-4}, doi = {10.1007/978-3-319-63450-010.1007/978-3-319-63450-0_11}, url = {http://link.springer.com/10.1007/978-3-319-63450-0}, author = {Stowell, Dan} } @article {47717, title = {On-Bird Sound Recordings: Automatic Acoustic Recognition of Activities and Contexts}, journal = {IEEE/ACM Transactions on Audio, Speech, and Language Processing}, volume = {25}, year = {2017}, month = {Jan-06-2017}, pages = {1193 - 1206}, abstract = {

We introduce a novel approach to studying animal behavior and the context in which it occurs, through the use of microphone backpacks carried on the backs of individual free-flying birds. These sensors are increasingly used by animal behavior researchers to study individual vocalizations of freely behaving animals, even in the field. However, such devices may record more than an animal\&$\#$39;s vocal behavior, and have the potential to be used for investigating specific activities (movement) and context (background) within which vocalizations occur. To facilitate this approach, we investigate the automatic annotation of such recordings through two different sound scene analysis paradigms: A scene-classification method using feature learning, and an event-detection method using probabilistic latent component analysis. We analyze recordings made with Eurasian jackdaws (Corvus monedula) in both captive and field settings. Results are comparable with the state of the art in sound scene analysis; we find that the current recognition quality level enables scalable automatic annotation of audio logger data, given partial annotation, but also find that individual differences between animals and/or their backpacks limit the generalization from one individual to another. we consider the interrelation of \“scenes\” and \“events\” in this particular task, and issues of temporal resolution.

}, keywords = {acoustics, birds, context, event detection, image analysis, microphones, pattern recognition, speech, zoology}, issn = {2329-9290}, doi = {10.1109/TASLP.2017.2690565}, url = {http://ieeexplore.ieee.org/document/7933044/}, author = {Stowell, Dan and Gill, Lisa F.} }