32nd European Signal Processing Conference (EUSIPCO), proceedings of 32nd European Signal Processing Conference (EUSIPCO 2024) Efficient and precise annotation of local structures in data. :902-906

Natural Sciences, Mathematical Sciences, Probability Theory and Statistics, Naturvetenskap, Matematik, Sannolikhetsteori och statistik, Engineering and Technology, Electrical Engineering, Electronic Engineering, Information Engineering, Signal Processing, Teknik, Elektroteknik och elektronik, Signalbehandling

We propose an adaptive change point detection method (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 activations 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. We derive query segments to guide the weak label annotator towards strong labels, using change point detection on these probabilities. We show that it is possible to derive strong labels of high quality with a limited annotation budget, and show favorable results for A-CPD when compared to two baseline query segment strategies.