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 (e.g. 20 hours) to do manually and has too few prior labeled examples (e.g. one) to train a state-of-The-Art machine audio labeling system. We performed a human-subject study to validate its effectiveness and the results showed that our tool helped participants label all target sound events within a recording twice as fast as labeling them manually.