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@techreport{Emre Cakir2017,
author = "Emre Cakir and Konstantinos Drossos and Tuomas Virtanen",
abstract = "In this paper, we focus on bird audio detection in short audio
segments (namely 10 seconds) using stacked convolutional
and recurrent neural networks. The evaluation data for this
task was recorded in an acoustic soundscape different from
the development data, thus motivating to work on methods
that are generic and context independent. Data augmentation
and regularization methods are proposed and evaluated in this
regard. Area under curve (AUC) measure is used to compare
different results. Our best achieved AUC measure on
five cross-validations of the development data is 95.3% and
88.41% on the unseen evaluation data.",
keywords = "Bird audio detection; convolutional recurrent neural network",
title = "{QMUL} bird audio detection challenge 2016 ",
url = "https://machine-listening.eecs.qmul.ac.uk/wp-content/uploads/sites/26/2017/01/adavanne.pdf",
year = "2017",
}