Software & Data Downloads — SEBBs

Python implementation for the prediction of sound event bounding boxes (SEBBs). SEBBs are one-dimensional bounding boxes defined by event onset time, event offset time, sound class and a confidence. They represent sound event candidates with a scalar confidence score assigned to it. We call it (1d) bounding boxes to highlight the similarity to the (2d) bounding boxes typically used for object detection in computer vision.

With SEBBs the sensitivity of a system can be controlled without an impact on the detection of an events' on- and offset times, which the previous frame-level thresholding approaches suffer from.

Access software at https://github.com/merlresearch/sebbs.