02790nas a2200217 4500008004100000245012400041210006900165520202600234653002802260653001702288653001702305653002902322653001902351653002302370100002402393700002402417700001602441700002402457700002402481856006702505 2020 eng d00ahardRain: An R package for quick, automated rainfall detection in ecoacoustic datasets using a threshold-based approach0 ahardRain An R package for quick automated rainfall detection in 3 a

The increasing demand for cost-efficient biodiversity data at large spatiotemporal scales has led to an increase in the collection of large ecoacoustic datasets. Whilst the ease of collection and storage of audio data has rapidly increased and costs fallen, methods for robust analysis of the data have not developed so quickly. Identification and classification of audio signals to species level is extremely desirable, but reliability can be highly affected by non-target noise, especially rainfall. Despite this demand, there are few easily applicable pre-processing methods available for rainfall detection for conservation practitioners and ecologists. Here, we use threshold values of two simple measures, Power Spectrum Density (amplitude) and Signal-to-Noise Ratio at two frequency bands, to differentiate between the presence and absence of heavy rainfall. We assess the effect of using different threshold values on Accuracy and Specificity. We apply the method to four datasets from both tropical and temperate regions, and find that it has up to 99% accuracy on tropical datasets (e.g. from the Brazilian Amazon), but performs less well in temperate environments. This is likely due to the intensity of rainfall in tropical forests and its falling on dense, broadleaf vegetation amplifying the sound. We show that by choosing between different threshold values, informed trade-offs can be made between Accuracy and Specificity, thus allowing the exclusion of large amounts of audio data containing rainfall in all locations without the loss of data not containing rain. We assess the impact of using different sample sizes of audio data to set threshold values, and find that 200 15 s audio files represents an optimal trade-off between effort, accuracy and specificity in most scenarios. This methodology and accompanying R package ‘hardRain’ is the first automated rainfall detection tool for pre-processing large acoustic datasets without the need for any additional rain gauge data.

10aAcoustic pre-processing10abioacoustics10aecoacoustics10aEnvironmental monitoring10aRain detection10aSoundscape ecology1 aMetcalf, Oliver, C.1 aLees, Alexander, C.1 aBarlow, Jos1 aMarsden, Stuart, J.1 aDevenish, Christian uhttps://linkinghub.elsevier.com/retrieve/pii/S1470160X1930787302455nas 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/S1470160X16307117