02455nas a2200241 4500008004100000022001300041245006400054210006400118260001600182300001300198490000700211520173400218653001701952653002901969653002901998653001902027653002302046100001802069700001902087700001902106700002202125856006602147 2017 eng d a1470160X00aAutomatic identification of rainfall in acoustic recordings0 aAutomatic identification of rainfall in acoustic recordings cJan-04-2017 a95 - 1000 v753 a

The rainfall regime is one of the main abiotic components that can cause modifications in the breeding activity of animal species. It has a direct effect on the environmental conditions, and acts as a modifier of the landscape and soundscape. Variations in water quality and acidity, flooding, erosion, and sound distortion are usually related with the presence of rain. Thereby, ecological studies in populations and communities would benefit from improvements in the estimation of rainfall patterns throughout space and time.

In this paper, a method for automatic detection of rainfall in forests by using acoustic recordings is proposed. This approach is based on the estimation of the mean value and signal to noise ratio of the power spectral density in the frequency band in which the sound of the raindrops falling over the vegetation layers of the forest is more prominent (i.e. 600–1200 Hz). The results of this method were compared with human auditory identification and data provided by a pluviometer. We achieved a correlation of 95.23% between the data provided by the pluviometer and the predictions of a regression model. Furthermore, we attained a general accuracy between 92.90% and 99.98% when identifying different intensity levels of rainfall on recordings.

Nowadays, passive monitoring recorders have been extensively used to study of acoustic-based breeding processes of several animal species. Our method uses the signals acquired by these recorders in order to identify and quantify rainfall events in short and long time spans. The proposed approach will automatically provide information about the rainfall patterns experienced by target species based on audio recordings.

10abioacoustics10aEnvironmental monitoring10aPrecipitation measuremen10aRain detection10aSoundscape ecology1 aBedoya, Carol1 aIsaza, Claudia1 aDaza, Juan, M.1 aLópez, José, D. uhttp://linkinghub.elsevier.com/retrieve/pii/S1470160X1630711702678nas a2200253 4500008004100000022001300041245010700054210006900161260001600230300001400246490000700260520191800267653001302185653001602198653001302214653002702227653001502254100001702269700001802286700001802304700001802322700001802340856006602358 2017 eng d a1470160X00aTesting the performances of automated identification of bat echolocation calls: A request for prudence0 aTesting the performances of automated identification of bat echo cJan-07-2017 a416 - 4200 v783 a

Echolocating bats are surveyed and studied acoustically with bat detectors routinely and worldwide, yet identification of species from calls often remains ambiguous or impossible due to intraspecific call variation and/or interspecific overlap in call design. To overcome such difficulties and to reduce workload, automated classifiers of echolocation calls have become popular, but their performance has not been tested sufficiently in the field. We examined the absolute performance of two commercially available programs (SonoChiro and Kaleidoscope) and one freeware package (BatClassify). We recorded noise from rain and calls of seven common bat species with Pettersson real-time full spectrum detectors in Sweden. The programs could always (100%) distinguish rain from bat calls, usually (68–100%) identify bats to group ( Nyctalus/Vespertilio/Eptesicus, Pipistrellus, Myotis, Plecotus, Barbastella ) and usually (83–99%) recognize typical calls of some species whose echolocation pulses are structurally distinct ( Pipistrellus pygmaeus , Barbastella barbastellus ). Species with less characteristic echolocation calls were not identified reliably, including Vespertilio murinus (16–26%), Myotis spp. (4–93%) and Plecotus auritus (0–89%). All programs showed major although different shortcomings and the often poor performance raising serious concerns about the use of automated classifiers for identification to species level in research and surveys. We highlight the importance of validating output from automated classifiers, and restricting their use to specific situations where identification can be made with high confidence. For comparison we also present the result of a manual identification test on a random subset of the files used to test the programs. It showed a higher classification success but performances were still low for more problematic taxa.

10aBiosonar10amethodology10asoftware10aspecies identification10aultrasound1 aRydell, Jens1 aNyman, Stefan1 aEklöf, Johan1 aJones, Gareth1 aRusso, Danilo uhttp://linkinghub.elsevier.com/retrieve/pii/S1470160X17301401