Sound Event Detection on Imbalanced Data using Spectral Entropy Active Learning

Abstract

Data imbalance is a detrimental factor in training Sound Event Detection (SED) systems. Long duration events are over-represented compared to short duration, minority events, and tend to overwhelm training. In addition, collecting and annotating sound event datasets is a laborious and expensive task. This work presents Spectral Entropy Active Learning (SEAL) to address both these problems. By exploiting the spectral characteristics of the audio, SEAL enables selection of short duration sounds which typically have low spectral entropy, thereby improving sampling distribution and increasing overall system performance. SEAL is compared against the state-of-the art Asymmetric Focal Loss (AFL) and Unified Loss Function (ULF) methods, and it surpasses AFL F1-Micro performance by 0.31 percentage points, while requiring only 65% of the dataset to be labelled. Although ULF shows slightly better over all accuracy, it generates twice as many false positives as SEAL. Similarly, SEAL demonstrates better overall and class wise performance than reference Active Learning methods, such as Random Sampling, K-Medoids, and Least Confidence. The findings further suggest that for SED on imbalanced data, selection strategies that maximise the sampling distribution are more effective than uncertainty-based approaches. Future work will focus on improving SEAL’s low labelling budget performance by integrating adaptive sample reweighting techniques, such as those used in AFL and ULF.