01560nas a2200133 4500008004100000020001800041245007100059210006900130260005300199520107200252100003501324700001901359856004801378 2018 eng d a978145036482900aAutomatic Ecosystem Identification Using Psychoacoustical Features0 aAutomatic Ecosystem Identification Using Psychoacoustical Featur aUnion, NJ, USANew York, New York, USAbACM Press3 a

The recent changes in worldwide ecosystems require constant monitoring and actions to prevent habitat destruction and mass extinction of species. The development of Passive Acoustic Monitoring (PAM) techniques has allowed the study of ecosystem dynamics, producing no harm to them. In this study, a relation between psychoacoustical features and ecosystem degradation was explored. Psychoacoustical features are used mainly for urban soundscapes categorization and they are based on human subjective perception of sound; however, they provide a starting framework for linking soundscape to spatial structure. To map soundscape to ecosystem health, an artificial neural network (ANN) and three different types of support vector machines (SVM) were tested for automatic ecosystem type identification using the selected features. Three out of four models leaded to good results, but as future work, it is suggested to adapt psychoacoustical features to a more neutral auditory system so the analysis includes more species perception of the ecosystem soundscape.

1 aDuque-Montoya, Diana, Carolina1 aIsaza, Claudia uhttp://dl.acm.org/citation.cfm?doid=324325002455nas 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