Deep Active Audio Feature Learning in Resource-Constrained Environments
Md Mohaimenuzzaman Christoph Bergmeir Bernd Meyer
The scarcity of labelled data makes training Deep Neural Network (DNN) models
in bioacoustic applications challenging. In typical bioacoustics applications,
manually labelling the required amount of data can be prohibitively expensive.
To effectively identify both new and current classes, DNN models must continue
to learn new features from a modest amount of fresh data. Active Learning (AL)
is an approach that can help with this learning while requiring little
labelling effort. Nevertheless, the use of fixed feature extraction approaches
limits feature quality, resulting in underutilization of the benefits of AL. We
describe an AL framework that addresses this issue by incorporating feature
extraction into the AL loop and refining the feature extractor after each round
of manual annotation. In addition, we use raw audio processing rather than
spectrograms, which is a novel approach. Experiments reveal that the proposed
AL framework requires 14.3%, 66.7%, and 47.4% less labelling effort on
benchmark audio datasets ESC-50, UrbanSound8k, and InsectWingBeat,
respectively, for a large DNN model and similar savings on a
microcontroller-based counterpart. Furthermore, we showcase the practical
relevance of our study by incorporating data from conservation biology
projects.