@inproceedings{c8333ade016246f8897247da5d13b1ad,
title = "Automatic forest wood logging identification based on acoustic monitoring",
abstract = "In this paper we describe a scheme for automatic identification of wood logging activity in forest based on acoustic surveillance. Specifically, we evaluate five machine learning classification algorithms using several audio descriptors for the identification of chainsaw wood logging sounds in the noisy environment of a forest. Different environmental noise interference levels, in terms of sound-to-noise ratio, were considered and the best performance was achieved using support vector machines.",
keywords = "Audio based surveillance, Audio processing, Biodiversity monitoring, Classification",
author = "Iosif Mporas and Michael Paraskevas",
year = "2016",
month = may,
day = "18",
doi = "10.1145/2903220.2903258",
language = "English",
series = "ACM International Conference Proceeding Series",
publisher = "ACM Press",
editor = "Antonis Bikakis and Dimitrios Vrakas and Nick Bassiliades and Ioannis Vlahavas and George Vouros",
booktitle = "9th Hellenic Conference on Artificial Intelligence, SETN 2016",
address = "United States",
note = "9th Hellenic Conference on Artificial Intelligence, SETN 2016 ; Conference date: 18-05-2016 Through 20-05-2016",
}