Data-efficient weakly supervised learning for low-resource audio event detection using deep learning

By Veronica Morfi, Dan Stowell. 2018

15 citations recorded by the source corpus.

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

Subfields: Computer Vision and Pattern Recognition, Signal Processing

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Citations in the corpus

FSD50K: An Open Dataset of Human-Labeled Sound Events (2020, background): “The former approach implies two issues: i) in training, the weak labels must be inherited by every patch (a practice called false strong labeling in [79]), which can generate false positives if the label is not active in a given patch; ii) in evaluation, patch-level scores must be aggregated into cl”
Learning Sound Event Classifiers from Web Audio with Noisy Labels (2019, background): “A naive but common way of processing variable-length clips is to split them into several fixed-length patches, each inheriting the clip-level label (called false strong labeling in [21]).”