01392nas a2200241 4500008004100000245005800041210005700099520070900156653001800865653002800883653001700911653001900928653002300947653001700970653001500987653002101002100002001023700001401043700001901057700001901076700001701095856003801112 2019 eng d00aNIPS4Bplus: a richly annotated birdsong audio dataset0 aNIPS4Bplus a richly annotated birdsong audio dataset3 a

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.

10aaudio dataset10aAudio signal processing10abioacoustics10aBioinformatics10aBird vocalisations10aecoacoustics10aecosystems10arich annotations1 aMorfi, Veronica1 aBas, Yves1 aPamuła, Hanna1 aGlotin, Hervé1 aStowell, Dan uhttps://peerj.com/articles/cs-22301305nas a2200205 4500008004100000245005800041210005700099520071700156653001800873653002500891653001700916653001500933653002100948100002000969700001400989700001901003700001901022700001701041856004101058 2018 eng d00aNIPS4Bplus: a richly annotated birdsong audio dataset0 aNIPS4Bplus a richly annotated birdsong audio dataset3 a

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.

10aaudio dataset10abird vocalisa- tions10aecoacoustics10aecosystems10arich annotations1 aMorfi, Veronica1 aBas, Yves1 aPamuła, Hanna1 aGlotin, Hervé1 aStowell, Dan uhttps://bio.acousti.ca/nl/node/5327400423nas a2200121 4500008004100000020002200041245005400063210005400117260004400171300001400215100001700229856005500246 2018 eng d a978-3-319-63449-400aComputational Analysis of Sound Scenes and Events0 aComputational Analysis of Sound Scenes and Events aChambSpringer International Publishing a303 - 3331 aStowell, Dan uhttp://link.springer.com/10.1007/978-3-319-63450-002171nas a2200265 4500008004100000022001400041245008800055210006900143260001600212300001600228490000700244520143100251653001401682653001001696653001201706653002001718653001901738653001601757653002401773653001101797653001201808100001701820700001901837856004901856 2017 eng d a2329-929000aOn-Bird Sound Recordings: Automatic Acoustic Recognition of Activities and Contexts0 aOnBird Sound Recordings Automatic Acoustic Recognition of Activi cJan-06-2017 a1193 - 12060 v253 a

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'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.

10aacoustics10abirds10acontext10aevent detection10aimage analysis10amicrophones10apattern recognition10aspeech10azoology1 aStowell, Dan1 aGill, Lisa, F. uhttp://ieeexplore.ieee.org/document/7933044/