Automatic Ecosystem Identification Using Psychoacoustical Features

Publication Type:Conference Paper
Year of Publication:2018
Alkuperäinen tekijä:Duque-Montoya, Isaza
Conference Name:the International ConferenceProceedings of the International Conference on Pattern Recognition and Artificial Intelligence - PRAI 2018
Publisher:ACM Press
Conference Location:Union, NJ, USANew York, New York, USA
ISBN Number:9781450364829
Abstract:

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.

URL:http://dl.acm.org/citation.cfm?doid=3243250
DOI:10.1145/324325010.1145/3243250.3243251
BioAcoustica ID: 
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Scratchpads developed and conceived by (alphabetical): Ed Baker, Katherine Bouton Alice Heaton Dimitris Koureas, Laurence Livermore, Dave Roberts, Simon Rycroft, Ben Scott, Vince Smith