  In this work we propose an audio recording segmentation method based on an
adaptive change point detection (A-CPD) for machine guided weak label
annotation of audio recording segments. The goal is to maximize the amount of
information gained about the temporal activation's of the target sounds. For
each unlabeled audio recording, we use a prediction model to derive a
probability curve used to guide annotation. The prediction model is initially
pre-trained on available annotated sound event data with classes that are
disjoint from the classes in the unlabeled dataset. The prediction model then
gradually adapts to the annotations provided by the annotator in an active
learning loop. The queries used to guide the weak label annotator towards
strong labels are derived using change point detection on these probabilities.
We show that it is possible to derive strong labels of high quality even with a
limited annotation budget, and show favorable results for A-CPD when compared
to two baseline query strategies.
