  This paper proposes an active learning system for sound event detection
(SED). It aims at maximizing the accuracy of a learned SED model with limited
annotation effort. The proposed system analyzes an initially unlabeled audio
dataset, from which it selects sound segments for manual annotation. The
candidate segments are generated based on a proposed change point detection
approach, and the selection is based on the principle of mismatch-first
farthest-traversal. During the training of SED models, recordings are used as
training inputs, preserving the long-term context for annotated segments. The
proposed system clearly outperforms reference methods in the two datasets used
for evaluation (TUT Rare Sound 2017 and TAU Spatial Sound 2019). Training with
recordings as context outperforms training with only annotated segments.
Mismatch-first farthest-traversal outperforms reference sample selection
methods based on random sampling and uncertainty sampling. Remarkably, the
required annotation effort can be greatly reduced on the dataset where target
sound events are rare: by annotating only 2% of the training data, the achieved
SED performance is similar to annotating all the training data.
