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We propose a method to perform audio event detection under the commonconstraint that only limited training data are available. In training a deeplearning system to perform audio event detection, two practical problems arise.Firstly, most datasets are "weakly labelled" having only a list of eventspresent in each recording without any temporal information for training.Secondly, deep neural networks need a very large amount of labelled trainingdata to achieve good quality performance, yet in practice it is difficult tocollect enough samples for most classes of interest. In this paper, we proposea data-efficient training of a stacked convolutional and recurrent neuralnetwork. This neural network is trained in a multi instance learning settingfor which we introduce a new loss function that leads to improved trainingcompared to the usual approaches for weakly supervised learning. Wesuccessfully test our approach on two low-resource datasets that lack temporallabels.

Data-Efficient Weakly Supervised Learning for Low-Resource Audio Event Detection Using Deep Learning