9:20 - 9:40
Bongjun Kim
Northwestern University
I-SED: An Interactive Sound Event Detector
Tagging of sound events is essential in many research areas. However, finding sound events and labeling them within a long audio file is tedious and time-consuming. Building an automatic recognition system using machine learning techniques is often not feasible because it requires a large number of human-labeled training examples and fine tuning the model for a specific application. Fully automated labeling is also not reliable enough for all uses. We present I-SED, an interactive sound detection interface using a human-in-the-loop approach that lets a user reduce the time required to label audio that is tediously long to do manually and has too few prior labeled examples (e.g. one) to train a state-of-the-art machine audio labeling system.
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