Data-Centric Methods for Environmental Sound Classification With Limited Labels

Arctic boreal forests are warming at a rate 2-3 times faster than the global average. It is important to understand the effects of this warming on activities of animals that migrate to and within these environments annually to reproduce. Acoustic sensors can monitor a wide area relatively cheaply, producing large amounts of data. Yet, only a small proportion of the recorded data can be labeled by hand making it challenging to train high performing sound classifiers for ecoacoustic research. In this work, we explore data-centric methods for improving model performance by utilizing labels more efficiently. We show that indeed data augmentation for a DNN-based multi-label sound classifier yields a relative improvement (37%) in AUC performance. We are able to boost this further by 56% with a novel data valuation method. Our method estimates Shapley values for a multi-label DNN classifier enabling curation of a high quality training set and identification of data quality issues. We demonstrate that with our novel method, we can achieve these gains using as little as 40% of the labeled training data.