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/5327402861nas a2200277 4500008004100000022001300041245010200054210006900156260001600225300001400241490000600255520200300261653003302264653001702297653003102314653001202345653001702357653001102374100002302385700001402408700002502422700002702447700002102474700002202495856006602517 2016 eng d a2351989400aLarge-scale semi-automated acoustic monitoring allows to detect temporal decline of bush-crickets0 aLargescale semiautomated acoustic monitoring allows to detect te cJan-04-2016 a208 - 2180 v63 a

Monitoring biodiversity over large spatial and temporal scales is crucial to assess the impact of global changes and environmental mitigation measures. However, large-scale monitoring of invertebrates remains poorly developed despite the importance of these organisms in ecosystem functioning. The development of new recording techniques and new methods of automatic species recognition based on sound detection and easily applicable within a citizen-science framework, offers interesting possibilities. However, the value of such protocols has not been tested for the study of temporal trends on a large spatial scale.

We used an acoustic region-wide citizen-monitoring program of Orthoptera, conducted along roads, to assess the relevance of automatic species recognition methods to detect temporal trends while taking into account spatial and seasonal patterns of two Orthoptera species activity (Tettigonia viridissima Linnaeus, 1758, and Ruspolia nitidula Scopoli, 1786) at a large scale. Additionally, we tested the effect of climate and land-use variables on spatio-temporal abundance patterns using generalized linear mixed models. We found negative temporal trends for the two species across the survey period (2006–2012). The spatial variations were largely explained by the geoclimatic conditions and, to a lesser extent, by land use (negative effects of urbanization). The temporal variations were highly correlated to the climatic conditions of the year, and of the previous year (nonlinear effect of temperature, precipitation).

To our knowledge, this paper describes the first successful attempt to calculate large-scale temporal trends of insect populations on the basis of an automatic identification process of acoustic data. We argue that acoustic monitoring along roads, coupled with the automatic recognition of species sounds, offers several advantages for assessing Orthoptera biodiversity response to global changes and environmental measures.

10aautomated signal recognition10abioacoustics10acitizen monitoring program10aClimate10aTettigoniida10atrends1 aJeliazkov, Alienor1 aBas, Yves1 aKerbiriou, Christian1 aJulien, Jean-François1 aPenone, Caterina1 aLe Viol, Isabelle uhttp://linkinghub.elsevier.com/retrieve/pii/S235198941530032903079nas a2200145 4500008004100000245011600041210006900157260001600226520256900242100002302811700001402834700001602848700002002864856004902884 2017 eng d00aPotential for coupling the monitoring of bush-crickets with established large-scale acoustic monitoring of bats0 aPotential for coupling the monitoring of bushcrickets with estab cJan-01-20173 a

Monitoring biodiversity over large spatial and temporal scales is crucial for assessing the impact of global changes and environmental mitigation measures. However, large-scale monitoring of invertebrates remains poorly developed despite the importance of these organisms in ecosystem functioning. Exciting possibilities applicable to professional and citizen science are offered by new recording techniques and methods of semi-automated species recognition based on sound detection.


Static broad-spectrum detectors deployed to record throughout whole nights have been recommended for standardised acoustic monitoring of bats, but they have the potential to also collect acoustic data for other species groups. Large-scale deployment of such systems is only viable when combined with robust automated species identification algorithms. Here we examine the potential of such a system for detecting, identifying and monitoring bush-crickets (Orthoptera of the family Tettigoniidae). We use incidental sound recordings generated by an extensive citizen science bat survey and recordings from intensive site surveys to test a semi-automated step-wise method with a classifier for assigning species identities. We assess species’ diel activity patterns to make recommendations for survey timing and interpretation of existing nocturnal data sets and consider the feasibility of determining site occupancy.

Of six species of bush-crickets, the species classifier achieved over 85% accuracy for three, speckled bush-cricket, dark bush-cricket and Roesel's bush-cricket. It should be possible to automatically scan recordings for these species with minimal manual validation. Further refinement of the classifier is required for the three remaining species, in particular for the acoustically similar short-winged conehead and long-winged conehead. Diel activity patterns are species specific and it may be necessary to adjust the hours over which the detectors record to increase detection of key species, but this must be weighed against the costs in terms of increased memory and battery use and equipment security during daytime.

We conclude that with logistical support and centralised semi-automated species identification it is now possible for the public to contribute to large-scale acoustic monitoring of Orthoptera while recording bats. Further innovation of sound classifier algorithms is needed and would be aided by improved reference sound libraries from multiple locations spanning species’ ranges.

1 aNewson, Stuart, E.1 aBas, Yves1 aMurray, Ash1 aGillings, Simon uhttp://doi.wiley.com/10.1111/2041-210X.12720