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.

该研究提出了一种基于自适应变点检测（A-CPD）的音频切分方法，用于机器辅助对音频录音片段进行弱标签注释，旨在最大程度地获取关于目标声音的时间激活信息，并通过有限的注释资源实现高质量的强标签。

基于自适应变点检测和主动学习的从弱到强声音事件标签转换